Trading cryptocurrency without trusted third-parties (part I)

[Full disclosure: the author works on security for a cryptocurrency exchange]

The collapse of Mt. Gox in 2014 and its aftermath has inspired a healthy dose of skepticism towards storing cryptocurrency with online services. It has also inspired the search for decentralized exchange models where the functionality provided by MtGox can be realized without a single point-of-failure where all risk is concentrated. While the mystery of what went on at Mt Gox remains unresolved to this day, blockchain designs have continued to evolve. Bitcoin itself has not changed much at the protocol level, although it added a couple of new instructions to the scripting language. More significant advances happened with the introduction of segregated-witness, along with the emergence of so-called “layer 2” solutions for scaling such as the Lightning Network. Even more promising is the emergence of alternative blockchains capable of expressing more complex semantics, most notably Ethereum with its Turing-complete smart-contract language. This makes it a good time to revisit the problem of decentralized cryptocurrency exchange without the concentration risk created by storing funds.

Answering this question in turn requires reexamining the purpose of an exchange. At the simplest level, an exchange connects buyers and sellers. Sellers post the quantity they are willing to part with and a price they are willing to accept. Buyers in turn place bids to purchase a specific quantity at a price of their choosing. When these two sides “cross”—the bid meets or exceeds an ask— a trade is executed. The exchange facilitates the transfer of assets in both directions, delivering assets to the buyer while compensating the seller with the funds provided by the buyer.

In an ideal world where everyone is good for their word, this arrangement does not require parking any funds with the exchange.  If Alice offers to sell 1BTC and Bob has agreed to purchase that for $1200, we can count on Alice to deliver the cryptocurrency and Bob to send the US dollars. In this hypothetical universe, they do not have to place funds in escrow with the exchange or for that matter any other third-party. Bob can wire fiat currency to Alice’s bank-account and Alice sends Bitcoin over the blockchain to Bob’s address. In reality of course people frequently deviate from expected protocol, violate contractual obligations or engage in outright fraud. Perhaps Bob never had the funds to begin with or he had a change of heart after finding a cheaper price on another exchange after agreeing to the trade with Alice.

These are examples of counter-party risk. It becomes increasingly unmanageable at scale. It would be one thing if Alice and Bob happen to know each other, or expect to be doing business continuously—in these scenarios “defecting” and trying to cheat the other side becomes counterproductive. With thousands of participants in the market and interactions between any pair being infrequent, there is not much of an opportunity to build up a reputation. It is infeasible for everyone to keep tabs on the trustworthiness of every potential counter-party they may be trading with, or to disadvantage new participants because they have no prior history to evaluate.

The standard model for exchanges provides one possible solution to this problem: Alice and Bob both deposit their funds with the exchange. The exchange is responsible for ensuring that all orders are fully covered by funds under custody. Using the example of BTC/USD trading, Alice can only offer to sell Bitcoin she has stored at the exchange and Bob can only place buy orders that his fiat balance can cover. Bob can be confident that the assets he just bid on are not phantom-Bitcoins that may fail to materialize after the trade completes. Likewise Alice knows she is guaranteed to receive USD regardless of which customer ends up being paired with her order.

The counter-party risk is mitigated but only at the expense of creating new challenges. In this model, the exchange becomes a custodian funds for everyone participating in the market. Aside from the obvious risk of a MtGox-type implosion, it creates a liquidity problem for these actors: their funds are tied up. Consider that a trader will be interested in betting on multiple cryptocurrencies across multiple exchanges. Even within a single trading pair such as USD/BTC, there are significant disparities in prices across exchange, creating arbitrage opportunities. But exploiting such disparities requires either maintaining positions everywhere or rapid funds movement between exchanges. Speed of Bitcoin movement is governed by mining time—which is an immutable property of the protocol, fixed at 10 minutes on average— and competition against other transactions vying for scarce room in the next block. In principle fiat currency can be moved much faster using the Federal Reserve wire system but that too depends on the implementation of wire transfer functionality at each exchange. All of this spells increased friction for moving in/out of markets, as well as greater amount of capital committed at multiple exchanges in anticipation of trading opportunities.

Is it possible to eliminate counter-party risk without introducing these inefficiencies? Over the years, alternative models have been put forward for trading cryptocurrency while eliminating or at least greatly reducing the concentration of risk. For example Bitsquare bills itself as a decentralized exchange, noting that it does not hold any user funds. Behind the scenes, this is achieved by relying on trusted arbitrators to mediate exchanges and resolve disputes:

“If Trader A fails to confirm the receipt of a national currency transfer within the allotted time (e.g. six days for SEPA, one day for OKPay, etc.), a button to contact the arbitrator will appear to both traders. Trader B will then be able to submit evidence to the arbitrator that he did, in fact, send the national currency. Alternatively, if Trader B never sent the national currency, Trader A will be able to submit evidence to the arbitrator that the funds were never received.”

In other words, counter-party risk is managed by having humans in the loop acting as trusted third-parties, rendering judgment on which side of the trade failed to live up to their obligations. The system is designed with economic incentives to encourage following the protocol: backing out of a trade or failing to deliver promised asset does result in loss of funds for the party at fault. (Interesting enough, the punitive damages are rewarded to the arbitrator, rather than the counter-party inconvenienced by that transgression. It is practically in the interest of arbitrators to have participants misbehave, since they get to collect additional payments above and beyond their usual fee.) Arbitrators are also required to post a significant bond, which they will lose if they are caught colluding with participants to deviate from the protocol.

Even with the fallibility of human arbitrators, this system achieves the stated goal of diffusing risk: instead of relying on the exchange to safeguard all funds, participants rely on individual arbitrators to watch over much smaller amounts at stake in specific trades. But there are other types of risk this arrangement can not hedge against, notably that of charge-backs. This is a very common challenge when trying to design a system for trading fiat currency against cryptocurrency. Blockchain transfers are irreversible by design. By contrast, most common options for transmitting fiat can be reversed in case they are disputed. For example, if an ACH transfer is initiated using stolen online banking credentials, the legitimate owner can later object to this transaction by notifying their bank in writing. Depending on the situation, they may have up to 60 days to do so. If the bank is convinced that the ACH was unauthorized, they can reverse the ACH transfer. What this means is that Alice can face an unpleasant surprise many weeks after releasing Bitcoin to Bob. Bob— or whoever owns the account Bob used to send those funds— can recover the funds Alice received as proceeds, leaving her holding the proverbial bag, since she has no recourse to clawing back bitcoin.

Also note that functionality is somewhat reduced compared to a traditional exchange. As the FAQ notes, settlement phase can take multiple days depending on how fiat-currency is sourced. Bitcoin purchased this way is not available immediately; it can not be transferred to a personal wallet or used to pay for purchase. That’s a stark contrast from a conventional exchange where settlement is nearly instantaneous. Once the trade has executed, either side can take their USD or BTC, and use it right away, withdraw to another address or place orders for a different pair such as BTC/ETH. In P2P models, availability of funds depends on the fiat payment clearing to the satisfaction of the counter-party, and that person getting around to sending the cryptocurrency. High-frequency trading in the blink of an eye, this is not.

Looking beyond fielded systems to what is possible in theory, we can ask whether there are any results in cryptography that can provide a basis for truly decentralized, trust-free trading of currencies. Here the news is somewhat mixed.

This problem in the abstract has been studied under the rubric of fair-exchange. A fair-exchange protocol is an interactive scheme for two parties to exchange secrets in an all-or-nothing manner. That is, Alice has some secret A and Bob has a different secret B. The goal is to design a protocol such that after a number of back-and-forth messages, one of two outcomes happen:

  • Alice has obtained B and Bob has obtained A.
  • Neither one has learned anything new

This protocol is “fair” because neither side comes out ahead in any outcome. By contrast, if there was an outcome where Alice learns B and Bob walks away empty-handed, the result would be decidedly unfair to Bob. There is a nagging question here of how participants can verify the value and/or legitimacy of their respective secrets ahead of time. But assuming that problem can be solved, such protocols would be incredibly useful in many contexts including cryptocurrency. For example if A happens to be a private-key controlling an Ethereum account while B controls some bitcoin, one could implement BTC/ETH trade by arranging for an exchange of those secrets.

Now the bad news: there is an impossibility result proving that such protocols can not exist. A 1999 paper titled “On the Impossibility of Fair Exchange without a Trusted Third Party” shows exactly what the title says: there exists no protocol which can achieve the above objectives with only Alice and Bob in the picture. There must be an impartial referee Trent such that if either Alice or Bob deviate from the protocol, Trent can intervene and force the protocol to produce an equitable outcome. The silver lining is that the negative result does not rule out so-called optimistic fair-exchange, where third-party involvement is not required provided everyone duly performs their assigned role. The referee is only asked to intervene when one side deviates from the expected sequence. But “hope is not a method,” as the saying goes. Given the sordid history of scams and fraudulent behavior in cryptocurrency, counting on everyone to follow the protocol is naive.

On paper this does not bode well for the vision of implementing trust-free exchange. But this is where blockchains provide a surprising assist: it has been observed that the blockchain itself can assume the role of an impartial third-party. Here is a simple example from 2014 where Andrychowicz et al. leverage Bitcoin to improve on a well-known cryptographic protocol for coin-flipping. Slightly simplified, the original protocol proceeds this way:

  1. Alice and Bob both pick a random bit string
  2. They “commit” to their strings, by computing a cryptographic hash of that value and publishing that commitment
  3. After both have committed, each side “opens” the commitment by revealing the original string
  4. Since the hash function is public, both sides can check that commitments were opened correctly
  5. Alice and Bob now compare the least-significant bits of the two unveiled strings. If those bits are identical, Alice wins the coin-toss. Otherwise Bob wins.

This is great in theory but what happens if Bob stops at step #3? After all, once Alice reveals her commitment, Bob has full-knowledge of both strings. He can already see the writing on the wall if he lost. That would be a great time to feign network connection issues, Windows 10 upgrade or any other excuse to stop short of revealing his original choice to prevent Alice from obtaining the information necessary to prove she won the coin-toss.

Enter Bitcoin. Blockchains allow defining payments according to predetermined rules. Those rules have fixed capabilities; they can not magically reach out into the real world, dive-tackle Bob and compel him to continue protocol execution. But they can arrange for the next best outcome: make it economically costly for Bob to deviate from the protocol. Specifically Alice and Bob both must commit some funds as good-faith deposit at the outset. To reclaim their money, they must open the commitment and reveal their original bit-string by a set deadline. If either side fails to complete the protocol in a timely manner, the other party can claim their deposit. This outcome is “fair” in the sense that Bob backing out (regardless of how creative his excuse is) results in Alice being compensated.

Variants of this idea can be used to design protocols for fair exchange of crypto-currency between different blockchains. The next post will look at a specific example involving Bitcoin and Ethereum. This is admittedly a case of looking for keys under the lamp-post; developing protocols to exchange crypto-currencies is much easier than trading against fiat. Blockchain payments proceed according to well-defined mathematical structures. By contrast, fiat movement involves notions such as ACH or wire-transfers that are extrinsic to the blockchain, and not easily mapped to those constructs.

[continued in part II]


Bitcoin and the C-programmer’s disease

Revenge of the C programmer

The Jargon File, a compendium of colorful terminology from the early days of computing later compiled into “The New Hacker’s Dictionary” defines the C programmer’s disease as the tendency of software written in that particular programming language to feature arbitrary limits on its functionality:

C Programmer’s Disease: noun.
The tendency of the undisciplined C programmer to set arbitrary but supposedly generous static limits on table sizes (defined, if you’re lucky, by constants in header files) rather than taking the trouble to do proper dynamic storage allocation. If an application user later needs to put 68 elements into a table of size 50, the afflicted programmer reasons that he or she can easily reset the table size to 68 (or even as much as 70, to allow for future expansion) and recompile. This gives the programmer the comfortable feeling of having made the effort to satisfy the user’s (unreasonable) demands, …

Imagine spreadsheets limited to 50 columns, word-processors that assume no document will exceed 500 pages or a social network that only lets you have one thousand friends. What makes such upper bounds capricious—earning a place in the jargon and casting aspersions on the judgment of C programmers everywhere— is that they are not derived from any inherent limitation of the underlying hardware itself. Certainly handling a larger document takes more memory or disk space. Even the most powerful machine will max out eventually. But software afflicted with this problem pays no attention to how much of either resource the hardware happens to possess. Instead the software designers in their infinite wisdom decided that no sane user needs more pages/columns/friends than what they have seen fit to define as universal limit.

It is easy to look back and make fun of these decisions with the passage of time, because they look incredibly short-sighted. “640 kilobytes ought to be enough for anybody!” Bill Gates allegedly said in reference to the initial memory limit of MS-DOS (although the veracity of this quote is often disputed.) Software engineering has thankfully evolved beyond using C for everything. High-level languages these days make it much easier to do proper dynamic resource allocation, obviating the need for guessing at limits in advance. Yet more subtle instances of hardwired limits keep cropping up in surprising places.

Blocked on a scaling solution

The scaling debate in Bitcoin is one of them. There is a fundamental parameter in the system, the so-called block-size, which has been capped at a magic number of 1MB. That number has a profound effect on how many transactions can take place, in other words how many times funds can be moved from one person to another, the sine qua non When there are more transactions available than blocks, congestion results: transactions take longer to appear in a block and miners can become more picky about which transactions to include.

Each transaction includes a small fee paid to miners. In the early days of the network, these fees were so astronomically low that Bitcoin was being touted as the killer-app for any number of problems with entrenched middlemen. (In one example, someone moved $80M paying only cents in fees.) Losing too much of your profit margin to credit-card processing fees? Accept Bitcoin and skip the 2-3% “tax” charged by Visa/Mastercard. No viable alternative to intrusive advertising to support original content online? Use Bitcoin micro-payments to contribute a few cents to your favorite blogger each time you share one of their articles. Today transactions can exceed ~$1 on average. No one is seriously suggesting paying for coffee in Bitcoin any longer, but some other scenarios such as cross-border remittances where much larger amounts are typically transferred with near-usurious rates charged by incumbents like Western Union remain economically competitive.

Magic numbers and arbitrary decisions

Strictly speaking the block-size cap is not a case of the C programmer’s disease. Bitcoin Core having been authored in C++ has nothing to do with the existence of this limit. To wit, many other parameters are fully configurable or scale automatically to utilize available resources on the machine where the code runs. The blocksize is not an incidental property of the implementation, it is a deliberate decision built into the protocol. Even alternative implementations written in other languages are required to follow it. The seemingly-innocuous limit was introduced to prevent disruption to the network caused by excessive blocks. In other words, there are solid technical reasons for introducing some limit. Propagating blocks over the network gets harder as their size increases, a problem acutely experienced by the majority of mining power which happens to be based in China and relying on high-latency networks behind the Great Firewall. Even verifying that a block is correctly produced is a problem, due to some design flaws in how Bitcoin transactions are signed. In the worst-case scenario the complexity of block verification scales quadratically: a transaction twice as large can take four times as much CPU time to verify. (A pathological block containing such a giant transaction was mined at least once, in what appears to have been a good-intentioned attempt by a miner to clean up previous errant transactions. Creating such a transaction is much easier than verifying it.)

In another sense, there is a case of the C programmer attitude at work here. Someone, somewhere made an “executive decision” that 1MB blocks are enough to sustain the Bitcoin network. Whether they intended that as a temporary stop-gap measure to an ongoing incident, to be revisited later with a better solution, or as an absolute ceiling for now and ever is open to interpretation. But one thing is clear: that number is arbitrary. From the fact that a limit must exist, it does not follow that 1MB is that hollowed number. There is nothing magical about this quantity to confer an aura of inevitable finality on the status quo. It is a nice, round number pulled out of thin-air. There was no theoretical model built to estimate the effect of block-size on system properties such as propagation time, orphaned blocks,  bandwidth required for a viable mining operation— the last one being critical to the idea of decentralization. No one solved a complex optimization problem involving varying block-sizes and determined that an even 1000000 bytes is the ideal number. That was not even done in 2010, much less in the present moment where presumably different, better network conditions exist around bandwidth and latency. If anything, when academic attention turned to this problem, initial results based on simulation suggested that the present population of nodes can accommodate larger blocks.

Blocksize and its discontents

Discontent around the blocksize limit grew louder in 2015, opening the door to one of the more acrimonious episodes in Bitcoin history. The controversy eventually coalesced around two camps. The opening salvo came from a group of developers who pushed for creating an incompatible version called Bitcoin XT, with a much higher limit: initially 20MB, later “negotiated” down to 8MB. Activating this version would require a disruptive upgrade process across the board, a hard-fork where the network risks splintering into two unless the vast majority of nodes upgrade. Serious disruption can result if a sizable splinter faction continues to run the previous version which rejects large blocks. Transactions appearing in these super-sized blocks would not be recognized by this group. In effect Bitcoin an asset itself would splinter into two. For each Bitcoin there would have been one“Bitcoin XT” you own on the extended ledger with large blocks and one garden-variety old-school Bitcoin owned on the original ledger. These two ledgers would start out identical but later evolve as parallel universes, diverging further with each transaction that appears on one chain without being mirrored in the other.

To fork or not to fork

If the XT logic for automatically activating a hard-fork sounds like a reckless ultimatum to the network, the experience of the Ethereum project removed any doubts on just how disruptive and unpredictable such inflection points can get. An alternative crypto-currency built around smart contracts, Ethereum had to undertake its own emergency hard-fork to bailout the too-big-to-fail DAO. The DAO (Distributed Autonomous Organization) was an ambitious project to create a venture capital firm as a smart-contract running on Ethereum with direct voting on proposal by investors. It had amassed $150M in funds until an enterprising crook noticed that the contract contained a security bug and exploited it to siphon funds away. The Ethereum Foundation sprung into action, arranging for a hard-fork to undo the security breach and restore stolen funds back to the DAO participants. But the rest of the community was unimpressed. Equating this action to crony-capitalism and bailout of failed institutions common in fiat currencies—precisely the interventionist streak that crypto-currencies were supposed to leave behind— a vocal minority declined to go along. Instead of going along with the fork, they dedicated their resources to keeping the original Ethereum ledger going, now rebranded as “Ethereum Classic.” To this day ETC survives as a crypto-currency with its own miners, its own markets for trading against other currencies (including USD) and most importantly its own blockchain. In that parallel universe, the DAO theft has never been reverted and the alternate ending of the DAO story is the thief riding off into the sunset holding bags of stolen virtual currency.

The XT proposal arrived on the scene a full year before Ethereum provided this abject lesson on the dangers of going full-speed ahead on contentious forks. But the backlash against XT was nevertheless swift. Ultimately one of its key contributors rage-quit, calling Bitcoin a failed experiment. One year after that prescient comment, Bitcoin price had tripled, proving Yogi Berra’s maxim about the difficulty of making predictions. But the scaling controversy would not go away. Blocks created by miners continued to edge closer to the absolute limit, fees required to get transactions into those blocks started to fluctuate and spike, as did confirmation times.

Meanwhile Bitcoin Core team quietly pursued a more cautious, conservative approach, opting for introducing non-disruptive scaling improvements, such as faster signature verification to improve block verification times. This path avoided creating any ticking time-bombs or implied upgrade-or-else threats for everyone in the ecosystem. But it also circumscribed limits on what types of changes could be introduced when maintaining backwards compatibility is a nonnegotiable design goal. The most significant of these improvement was segregated-witness. It moves part of transaction data outside the space allotted to transactions within a block. This also provides a scaling improvement of sorts, a virtual block-size increase without violating the sacred 1MB covenant: by slimming down the representation of transactions on the ledger, one could squeeze more of them into the same scarce space available in one block. The crucial difference: this feature could be introduced as soft-fork. No ultimatums to upgrade by a certain deadline, no risk of network-wide chaos in case of failure to upgrade. Miners indicate their intention to support segregated witness in the blocks they produce. The feature is activated when a critical threshold is reached. If anything segregated witness was too deferential to miner votes, requiring an unusually high degree of consensus at 95% before going into effect.

Beyond kicking the can down the road

At the time of writing, blocks signaling support for segregated witness plateaued around 30%. Meanwhile Bitcoin Unlimited (BU) inherited the crown from XT in pushing for disruptive hard-forks, by opening the door to miners voting on block size. It has gained enough support among miners that a contentious fork is no longer out of the question. Several exchanges have signed onto a letter describing how Bitcoin Unlimited would be handled if it does fork into a parallel universe, and at least one exchange has already started trading in futures about the fork.

Instead of trying to make predictions about how this stand-off will play out, it is better to focus on the long-term challenge of scaling Bitcoin. One-time increase in capacity  enabled by segregated witness (up to 2x, depending on assumptions about adoption rate and mix of transactions) is no less arbitrary than the original 1MB limit that all sides are railing against. Even BU with the implied of lack of limitations in the name turns out to cap blocksize at 256MB—not to mention that in a world where miners decide block size, it is far from clear that the result will be a relentless competition to increase it over time. Replacing one magic number pulled out of thin air with an equally bogus one that does not derive from any coherent reasoning built on empirical data is not a “scaling solution.” It is just an attempt to kick the can down the road. The same circumstances precipitating the current crisis—congested blocks, high and unpredictable transaction fees, periodic confirmation delays— will crop up again once network usage starts pushing against the next arbitrary limit.

Bitcoin needs a sustainable solution for scaling on-chain without playing a dangerous game of chicken with disruptive forks Neither segregated witness or Bitcoin Unlimited provides a vision for solving that problem. It is one thing to risk disruptive hard-forks once to solve the problem for good. It is irresponsible to engage in such brinkmanship as the standard operating procedure.


Extracting OTP seeds from Authy

An OTP app is an OTP app is an…

A recent post on the Gemini blog outlined changes to two-factor authentication (2FA) on Gemini, providing additional background on the Authy service. Past comments suggest there were common misconceptions about Authy, perhaps none more prominent than the assumption that it is based on SMS. Authy is a service which includes multiple options for 2FA: SMS, voice, mobile app for generating codes and OneTouch. At the same time a common question often asked is: “Can I use Google Authenticator or other favorite 2FA application instead?” Making that scenario work turns out to be a good way to gain insight into how Authy app itself operates under the covers.

Authy has mobile applications for Android, iOS as well as two incarnations for desktops: a Chrome extension and a Chrome application. All of them can generate one-time passcodes (OTP) to serve as second-factor when logging into a website. A natural question is how these codes are generated and whether they are compatible with other popular OTP applications such as Google Authenticator or Duo Mobile.

This is not a foregone conclusion and not all OTP-generation algorithms are identical. For example the earliest design in the market was SecurID by RSA Security. These were small hardware tokens with a seven-segment LCD display for showing numerical codes. RSA not only sold these tokens but also operated the service for verifying codes entered by users. For all intents and purposes, the tokens were a blackbox: the algorithm used to generate the codes was undocumented and users could not reprogram the tokens on their own. (That did not work out all that well when RSA was breached by nation-state attackers in 2011, resulting in the downstream compromise of RSA customers relying on SecurID— including notably Lockheed Martin.)

Carrying around an extra gadget for a single purpose limits usability, all but guaranteeing that solutions such as Secure ID are confined to “enterprise” scenarios— in other words, where employees have no say in the matter because their IT department decided this is how authentication works. Ubiquity of smart-phones made it possible to replace one-off special purpose gadgets with so-called “soft tokens:” mobile apps running on smartphones that can implement similar logic without the baggage of additional hardware.

Google Authenticator was among the first of these intended for mass market consumption, as opposed to the more niche enterprise scenarios. [Full disclosure: This blogger worked on two-factor authentication at Google, including as maintainer of the Android version of Google Authenticator] Early versions were open-sourced, although the version on Play Store diverged significantly after 2010 without releasing updates to source. Still looking at the code and surrounding documentation explains how OTPs are generated. Specifically it is based on two open standards:

  • TOTP: Time-based OTP as standardized in RFC 6238. Codes are generated by applying a keyed-hash function (specifically HMAC) to the current time, suitably quantized into intervals.
  • HOTP: HMAC-based OTP standardized by RFC 4226. As the RFC number suggests, this predates TOTP. Codes are generated by applying a keyed hash function to an incrementing counter. That counter is incremented each time an OTP is generated. (Incidentally the internal authentication system for Google employees— as opposed to end-users/customers— leveraged this mode rather than TOTP.)

The mystery algorithm

So what is Authy using? TOTP is a reasonable guess because Authy is documented to be compatible with Google Authenticator, and can in fact import seeds using the same URL scheme represented in QR-codes. But strictly speaking that only proves that Authy includes a TOTP implementation as subset of its functionality. Recall that Authy also includes a cloud-service responsible for provisioning seeds to phones; these are the “native” accounts managed by Authy, as opposed to stand-alone accounts where the user must scan a QR code. It is entirely conceivable that OTP generation for native Authy accounts follows some other standard. The fact that native Authy accounts generate 7-digit codes lends some support to that theory, since GA can only generate 6-digit codes. (Interestingly the URL scheme for QR codes allows 6 or 8 digits, but as the documentation points out GA ignores that parameter.)

Answering this question requires looking under the hood of the Authy app itself. In principle we can pick any version. This blog post uses the Chrome application as case study, because reviewing Chrome apps does not require any special software beyond the Developer Tools built into Chrome itself. No pulling APKs from the phone, no disassembling Dalvik binaries or firing up IDA Pro necessary.

There is another benefit to going after the Chrome application: since the end-goal is using an alternative OTP application to generate codes compatible with Authy, it is necessary to extract the secret-key used by the application. Depending on how keys are managed, this can be more challenging on a mobile device. For example some models of Android feature hardware-backed key storage provided by TrustZone, where keys are not extractable after provisioning. AN application can ask the hardware for specific operations to be performed with the key, such as signing a message as called for by TOTP. But it can not obtain raw bits of the key to ship them off-device. (While the security level of TrustZone is weak compared to an embedded secure element or TPM— hardened chips with real physical security— it still raises the bar against trivial software attacks.) By contrast browser applications are confined to standard Javascript interfaces, with no access to dedicated cryptographic hardware even if one exists on the machine.

Understanding the Chrome application

First install the Authy application from the Chrome store:


Next visit chrome://extensions and make sure Developer Mode is enabled:


Now open chrome://apps in another tab and launch the Authy app:


Back to the extensions tab and click on “main.html” to inspect the source code, switch to “Sources” tab, and expand the JS folder. There is one Javascript file here called “app.js” The code has been minimized and does not look very legible:


Luckily Chrome has an option to pretty-print the minimized Javascript, prominently advertised at the top. After taking up Chrome on that offer, the code becomes eminently readable  with many recognizable symbols. Searching for the string “totp” finds a function definition:


This function computes TOTP in terms of HOTP, which may seem puzzling at first— time-based OTP based on counter-mode OTP? The intuition is both schemes share the same pattern: applying a cryptographic function to some internal state represented by a positive number. In the case of HOTP that number is an incrementing counter, increasing precisely by 1 each time an OTP is generated. In the case of TOTP the number is based on current time, effectively fast-forwarded to skip any unused values.

Looking around this function there are other hints, such as default number of digits set to 7 as expected.  More puzzling is the default time-interval set to 10 seconds. TOTP uses a time interval to “quantize” time into discrete blocks. If codes were a function of a very precise timestamp measured down to the millisecond, it would be very difficult for the server to verify it without knowing exactly when it was generated with the same accuracy. It would also require both sides to have perfectly synchronized clocks. (Recall that OTPs are being entered by users, so it is not an option to include additional metadata about time.) To work get around this problem, TOTP implementations round the time down to the nearest multiple of an interval. For example if one were using 60-second intervals, the OTP code would be identical during an entire minute. Incidentally TOTP spec defines time as number of seconds since Unix epoch, so these intervals needs not start on an exact minute boundary.

The apparent discrepancy arises from the fact that Authy app displays a code for 20 seconds, suggesting it is good for that period of time. But the underlying generator is using 10 second intervals, implying that codes change after that. What is going on here? The answer is based on another trick used to make OTP implementations deal with clocks getting out of sync or delays in submitting OTP over a network. Instead of checking strictly against the current time interval, most servers will also check submitted OTP against a few preceding and following intervals. In other words, there is usually more than 1 valid OTP code at any given time, including those corresponding to times a few minutes back and a few minutes into the future. For this reason even a “stale” OTP code generated 20 seconds ago can still be accepted even when the current time (measured in 10-second intervals) has advanced one or two steps.

But we are jumping ahead— there is one more critical step required to verify that we are looking at the right function, that this is the code-path invoked by the Chrome app when generating OTPs. Easiest way to check involves setting a breakpoint in that function, by clicking on the line number:


Now we wait for the app to generate a code. Sure enough it freezes with a “paused in debugger” message:


Back to Chrome developer tools, where the debugger has paused on a breakpoint. The debugger has helpfully annotated the function parameters and will also display local variables if you hover over them:


Tracing the call a few steps further would show that “e” is the secret-seed encoded in hex, “r” is the sequence number and “o” is the number of digits. (More precisely it used to be the secret-seed; this particular Authy install has since been deleted. Each install receives a different seed, even for the same account.) Comparing “r” against the current epoch time shows that it is roughly one-tenth the value, confirming the hypothesis of 10 second intervals.

Recreating the OTP generation with another app

A TOTP generator is defined by three parameters:

  • Secret-seed
  • Time interval
  • Number of digits

Since we have extracted all three, we have everything necessary to generate compatible codes using another TOTP implementation. Google Authenticator has defined a URL scheme starting with otp:// for encoding these parameters, which is commonly represented as 2-dimensional QR code scanned with a phone camera. So in principle one can create such a URL and import the generator into Google Authenticator or any other soft-token application that groks the same scheme. (Most 2FA applications including Duo Mobile have adopted the URL scheme introduced by GA for compatibility, to serve as drop-in replacements.) One catch is the key must be encoded using the esoteric base32 encoding instead of the more familiar hex or base64 options; this can be done with a line of Python. The final URL will take the form:


Why 8 digits? There is a practical problem with Authy using 7 digits output. The specification for the URL scheme states that valid choices are 6 or 8. (Not that it matters, since Google Authenticator does not support any option other than 6.) Luckily we do not need exactly 7 digits. Due to how HOTP/TOTP convert the HMAC output into a sequence of digits, the correct “7-digit code” can be obtained from the 8-digit one by throwing away the first digit. Armed with this trick we can create an otp:// URL containing the secret extracted from Authy and specify 8 digits. Neither Google Authenticator or Duo Mobile were able to import an account using this URL but the FreeOTP app from RedHat succeeds. Here is the final result, showing screenshots captured from both applications around the same time.

Side-by-side OTP apps

On the left: Authy Chrome app; on the right: FreeOTP running in Android emulator

Note the extra leading digit displayed by FreeOTP. Because the generator is configured to output 8 digits, it outputs one more digit that needs to be ignored when using the code.

There is no “vulnerability” here

To revisit the original questions:

  1. What algorithm does Authy use for generating codes? We have verified that it is based on TOTP.
  2. Can a different 2FA application be used to generate codes? Sort of: it is possible to copy the secret seed from an existing Authy installation and redeploy it on a different, stand-alone OTP application such as FreeOTP.  (One could also pursue other avenues to getting the seed, such as leveraging the server API used by the provisioning process.)

One point to be very clear on: there is no new vulnerability here. When using the Authy Chrome application, it is a given that an attacker with full control of the browser can extract the secret seeds. Similar attacks apply on mobile devices: seeds can be scraped from a jailbroken iPhone or rooted Android phone; it is just a matter of reverse-engineering how the application stores those secrets. Even when hardware-backed key storage is used, the seeds are vulnerable at the point of provisioning when they are delivered from the cloud or initially generated for upload to the cloud.


[Updated Apr 27th to correct a typo]

Principle of least-privilege: looking beyond the insider risk

Second-law applied to access rights

“I have excessive privileges for accessing this system. Please reduce my access rights to avoid unintended risk to our users.”

That is a statement you are unlikely to hear from any engineer or operations person tasked with running an online service. Far more likely are demands for additional access, couched in terms of an immediate crisis involving some urgent task that can not be performed without a password, root access on some server or being added to some privileged group. The principle of least privilege states that people and systems should only be granted those permissions absolutely necessary to perform their job. Holding this line in practice is an uphill battle. Similar to the increase of entropy dictated by the second-law of thermodynamics, access rights inexorably seem to expand over time, converging on a steady state where everyone has access to everything.

That isn’t surprising given the dynamics of operating services. Missing rights are quickly noticed when someone going about their work runs into an abrupt “access denied” error. Excessive, unnecessary privileges have no such overt consequences. They are only identified after a painstaking audit of access logs and security policies. A corollary: it is easy to grant more access, it is much harder to take it away. The effects of removing existing privilege are difficult to predict. Will that break an existing process? Was this person ever accessing that system? No wonder that employee departures are about the only time most companies reduce access. Doing it any other time preemptively requires some data crunching: mine audit logs to infer who accessed some resource in the last couple of weeks and compare that against all users with access. But even that may not tell the whole story. For example, often access is granted to plan for a worst-case disaster scenario, when employees who carry out some role in the normal course of affairs are no longer able to perform that role and their backups— who may never have accessed the system until that point— must step in.

Part of the problem is scaling teams: a policy that starts out entirely consistent with least-privilege can become a free-for-all when amplified to a larger team. Small companies often have very little in the way of formal separation of duties between engineering and operations. In a startup of 5 employees, people naturally gravitate to being jack-of-all-trades. If any person may be asked to perform any task, it is not entirely unreasonable for everyone to have root/administrator rights on every server the company manages. But once that rule is applied reflexively for every new hire (“all engineers get root on every box”) as the company scales to 100 people, there is massive risk amplification. It used to be that only five people could cause significant damage, whether by virtue of inadvertent mistakes or deliberate malfeasance. Now there are a hundred people capable of inflicting that level of harm, and not all of them may even have the same level of security awareness as the founding group.

There is also a subtle, cultural problem with attempting to pare down access. It is commonly interpreted as signaling distrust. In the same way that granting access to some system implies that person can be trusted with access to resources on that system—user data, confidential HR records, company financials— retracting that privilege can be seen as a harsh pronouncement on the untrustworthiness of the same person.

This post is an attempt to challenge that perception, primarily by pointing out that internal controls are not only, or even primarily, concerned with insider risk. Following the logic of least-privilege hardens systems against the more common threats from external actors. More subtly it protects employees from becoming a high-value target for persistent, highly-skilled attackers.

Game of numbers

Consider the probability of an employee device being compromised by malware. This could happen through different avenues such as unwittingly installing an application that had been back-doored or visiting a malicious website with a vulnerable browser or plugin. Suppose that there is one in a thousand (0.1%) chance of this happening to any given employee in the course of a month when they are going about their daily routine surfing the web and installing new app/updates/packages from different locations. Going back to our hypothetical examples above, for the garage company with five engineers there is about 6% chance that at least one person will get their machine owned after a year. But the post-seed-round startup with 100 engineers is looking at odds of 70%—in other words, more likely than not.

If every one of those hundred engineers had unfettered access to company systems, all of those resources at risk. As the saying goes: if an attacker is executing arbitrary code on your computer, it is no longer your computer.  The adversary can use compromised hosts as stepping stone to access other resources such as internal websites, databases containing customer information or production system. Note that neither two-factor authentication nor hardware tokens help under this threat model. A patient attacker can simply wait for the legitimate user to scan their fingerprint, connect USB token/smart-card, push buttons, perform an interpretive dance or whatever other bizarre ritual is required for successful authentication, piggy-backing on the resulting session to access remote resources once the user initiates a connection.

That in a nutshell is the main problem with over-extended access policies: every person in the organization becomes a critical point of failure against external attacks. This is purely a matter of probabilities; it is not casting aspersions on whether employees in question are honest, well-meaning or diligent. The never-ending supply of Flash 0-day vulnerabilities affects hard-working employees just as much as it affects the slackers. Similarly malicious ads finding their way into popular websites pose a threat to all visitors running vulnerable software without discriminating against their intentions.

Painting a target on the home team

There is a more subtle reason that over-broad access rights are dangerous not only for the organization overall, but for the individuals carrying them. It may end up painting a target on those employees in the eyes of a persistent attacker. Recall that one of the distinguishing factors for a targeted attack is the extensive investment in mapping out the organization the adversary is attempting to infiltrate. For example, they may perform basic reconnaissance through open-source channels including LinkedIn or Twitter to identify key employees and organizational structure. Assuming that information about internal roles can not be kept secret indefinitely, one concludes that attackers will develop a good idea of which persons they need to go after in order to succeed. Exactly who ends up being under the virtual cross-hairs depends on their objectives.

For run of the mill financial fraud, high-level executives and accounting personnel are obvious targets. For instance the 2014 attack on BitStamp started out with carefully tailored spear-phishing messages to executives, followed by successfully impersonating those executives to request transfer of Bitcoin. FBI has issued a general warning about these scams last year warning that “… schemers go to great lengths to spoof company e-mail or use social engineering to assume the identity of the CEO, a company attorney, or trusted vendor. They research employees who manage money and use language specific to the company they are targeting, then they request a wire fraud transfer using dollar amounts that lend legitimacy.”

While the FBI attributes a disputed figure of $2.3B in losses to such scams, perhaps the more remarkable part of this trend is: these are relatively simple and broadly accessible attacks. There are no fancy zero-days or creative new exploits techniques involved requiring nation-state level of expertise. Yet even crooks carrying out these amateurish smash-and-grab operations were capable of mapping out the organization structure and homing in on the right individuals.

More dangerous than the get-rich-quick attackers are those pursuing long-term, stealth persistence for intelligence gathering. This class of adversary is less interested in hawking credit-card numbers than retaining long-term access to company systems for ongoing information gathering. For example they could be interested in customer data, stealing intellectual property or simply using the current target as stepping stone for the next attack. That level of access requires gaining deep access to company systems, not just reading the email inbox an executive or two. Emails alone are not enough when the objective is nothing less than full control of IT infrastructure.

This is where highly-privileged employees come into the picture. It is a conservative assumption that resourceful attackers will identify the key personnel to go after. (That need not happen all at once;  gaining initial access to an unprivileged account often allows looking around to identify other accounts.)  Those with root access on production systems where customer data is stored are particularly high-value targets. They provide the type of low-level access to infrastructure which permit deploying additional offensive capabilities such as planting malware which is not possible with access to email alone. Equally high-profile as targets are internal IT personnel with the means to compromise other employee machines. for example by using a centralized-management solution to deploy arbitrary code.

It is difficult to avoid the existence of highly privileged accounts. When something goes wrong on a server in the data-center, the assumption is that someone can remotely connect and get root access in order to diagnose/fix the problem. Similarly for internal IT: when an employee forgets their password or experiences problems with their machine, there is someone they can turn to for help and that person will be able to reset their password or log into their machine to troubleshoot. Those users with the proverbial keys to the kingdom are understood be in the cross-hairs. They will be expected to exercise greater degree of caution and maintain higher standards of operational security than the average employee. Their activity will be monitored carefully and any signs of unusual activity investigated promptly.

The challenge for an organization is keeping the size of that group “manageable” in relation to the that of the company itself. If everyone has root everywhere or everyone can effectively compromise every other person through shared infrastructure (sometimes such dependencies can be subtle) the entire company becomes one high value target. Any individual failure has far-reaching consequences. Every successful attack against one employee becomes a major incident potentially impacting all assets.

Setting the right example

Information security professionals can lead by example in pushing back against gratuitous access policies. It is very common for security teams to be among the worst-offenders in not following least privilege. When this blogger was in charge of security at Airbnb, everyone wanted to gift security team more access to everything: administrator role at AWS, SSH to production servers running the site and many other creative ways to access customer data. Mere appearance of the word “security” in job titles seems to confer a sense of infallibility: not only are these fellows deserving of ultimate trust with the most valuable company information, but they must also have perfect operational security practices and zero chance of falling victim to attacks that may befall the standard engineer. These assumptions are dangerous. By virtue of getting access, we all introduce risk to the system regardless of how good our opsec game may be. Comparing the incremental risk to benefits generated on a case-by-case basis is a better approach to crafting access policies. If someone is constantly exercising their new privileges to help their overloaded colleagues solve problems, the case is easy to make. If on the other hand the system is used infrequently or never—suggesting access rights were given out of a reflexive process born out of hypothetical future scenarios—the benefits are unclear. Meanwhile risks will increase, because the person is less likely to be familiar with sound security practices for correctly using that system. Making these judgment calls is part of sound risk management.


Still one click away? Lessons from Yahoo on lock-in & competition

[Full disclosure: This blogger worked on MSFT and Google security teams]

The tar pit of platform lock-in

“Our competitors are just one click away.”

That used to one of the oft-repeated slogans at Google. On its face, this is the type of cliched motivational line senior leadership likes to throw around for rallying the troop: warning against complacency, with visions of users walking away, lured by the siren song of a more nimble competitor. But read at another level, it was a subtle dig at MSFT and their business strategy. MSFT had become an industry juggernaut by relying on the lock-in effects created by the Windows platform. Once consumers bought a copy of Windows, they were caught hook-and-sinker in the entire ecosystem, buying more applications written for Windows. (Microsoft Office suite ranking near the top of that list did not hurt either.) Many of those applications were either not available for other platforms or the porting job was at best an after-thought- as is still the case for Office on OSX today. Trying to switch from this ecosystem to an alternative platform such as Macintosh OSX or Linux became the IT equivalent of getting out of the tar pit. In fact the challenges MSFT faced deprecating Windows XP suggest that even movement within the ecosystem can be a daunting challenge for participants.

Such lock-in effects are even more pronounced in enterprise software. A company with thousands of employees running Windows inevitably finds itself setting up Active Directory to manage that fleet. AD in turns comes with an array of auxiliary features, and before long the IT department is shelling out $$ for more Windows Server licenses to operate VPN services for remote-access, Sharepoint for internal collaboration, Exchange for hosting email and that is only the beginning. Coupled with a slew of proprietary undocumented protocols which discouraged emergence of competing implementations (at least until the EU settlement forced MSFT’s hand in open documentation) these dependencies all but assured that any attempt to migrate out of the ecosystem would be an expensive and painful project for any large enterprise.

Lower switching costs online?

Online services were supposed to be different— in theory. If Google search quality went downhill, it is not that difficult for users to surf over to a competing search engine to run the same query. Everything goes through a standard web browser: no new software to install, no dependencies to untangle, no compatibility nightmare involving other applications breaking because the user decided to search on Bing.

This is not to say that the market for search engines are immune to inertia in consumer preferences or brand-name effects. For years MSFT ran a campaign involving “blind comparisons” designed to prove that Bing search results were at least as good as, if not better than, Google. (Leading to the insider joke at MSFT that Bing stands for “But It’s Not Google” to explain why users continued to favor Google.) The campaign did not seem to have convinced many people outside Redmond, but at least it was predicated on a reasonable assumption: search-engine choices can be swayed. If consumers were convinced they had a better option, nothing prevented them from switching.

Except that assumption had long been under assault. Search-engine preferences were increasingly becoming part of software configuration with varying degrees of control. Once it became clear that search was strategic, software designers decided it was too important to leave it up to users to navigate to the right website and type a query. Instead search functionality was integrated into toolbars, web-browsers and in the case of Windows, the operating system itself. Consumers only had to type their query into a magical search field and results would come back. Part of that convenience involved a decision by software authors (as opposed to users) on which search engine gets to provide those answers. Not surprisingly queries from Google toolbar were routed to Google, those from Internet Explorer went to Bing and Yahoo toolbar seemingly routed to whoever paid Yahoo more that year. Naturally that lead to plenty of disgruntlement and accusations of anti-competitive behavior from the provider who did not come out on top. Regulators got involved. In the case of the European Union, an investigation prodded by Google resulted in MSFT agreeing to change Internet Explorer and forcing users to choose a search engine on first run. The micro-management did not stop there: to prevent any bias the list of search options had to be randomly ordered for each user. (Some behavioral economics experiments suggest consumers have a preference for picking the first or last object out of a line-up)

Random ordering of search engines in IE

Random ordering of search engines in IE

Personalization: lock-in through data

Particularly puzzling feature of this episode is that online search is not even a personalized service at its core. Google has aggressively promoted its ability to return search results tailored to each user, and not coincidentally encouraged/nagged/bribed users into staying logged-in online while performing those searches to build a more comprehensive history. But online search can be carried out fully anonymously, with the service provider having no idea about the person behind the query. Reasonable people may disagree to what extent this degrades quality of results. One data-point: there are search engines such as DuckDuckGo which promise to not save search history or engage in other privacy-infringing user tracking. The corollary is that switching search providers does not “leave behind” much with the previous provider. There is a surprising asymmetry in the value attached to search history. It is a priceless asset for search engines which they can use to mine for patterns and improve their own accuracy. On the other hand it is not something that users get attached to or wax nostalgic over, wondering “what was I searching for last Thanksgiving?” There is no concept of downloading your search history from one provider and uploading it to another to maintain continuity.

Holding users hostage

That brings up the subject of Yahoo Mail and the (possibly inadvertent) “hostage taking” the company engaged in by disabling email forwarding at a particularly inopportune moment in the middle of a PR crisis with users heading for the exits en masse. Unlike search, email is intrinsically personalized. Switching email providers comes with the prospect of leaving some resources behind. There is all the past archive of messages to begin with, which may run into the gigabytes, not a trivial amount to download. Some protocols such as IMAP can make it easier to retrieve all messages in bulk, although the consumer is still stuck with locally managing this stash—making sure it is properly backed-up, confidential messages encrypted etc. More subtly there is the email address itself. Registering for a new email address is easy; updating every place where the previous email address was used is hard. Luckily there is a standard solution for this: email forwarding. If Alice can forward all incoming messages from to her new account at, she can bid farewell to Yahoo and only use Google going forward. Meanwhile her friends and associates can continue writing to her former Yahoo address until they gradually update their address books. Message delivery will not be interrupted; Alice will receive the forwarded messages and reply from Gmail.

This is good news for consumers who want to switch providers. It is also good news for fostering competition among email providers to lure away users from their rivals. On the other hand, it is bad news for email providers who are on the losing side of that competition. Case in point, Yahoo. Plagued by a scandal over having sanctioned NSA mass-surveillance of all email, the company found itself facing a mass exodus of users, complete with step-by-step guides published in mainstream media explaining how to close a Yahoo account. (Not surprisingly, there is no link to closing an account from the Account page where one would actually expect to find it.)

Coincidentally around this time, the company decided to disable email-forwarding citing improvements in progress:

“While we work to improve it, we’ve temporarily disabled the ability to turn on Mail Forwarding for new forwarding addresses,”

In keeping with Hanlon’s razor, let us give Yahoo benefit of the doubt and assume that decision is indeed motivated by engineering concerns, as opposed to strategic maneuvering to hold users hostage until the PR crisis blows over. (Indeed the functionality was restored a few days later, prompting an Engadet headline to declare “you can finally leave.”) It is nonetheless a staggering demonstration of how deceptive that “one-click away” premise for competition can be. Many Yahoo users may have been outraged enough to register a new email account with Google or MSFT after reading the news but that is not the same abandoning ship altogether. As long as they are still receiving email at their Yahoo address and they can not forward those messages automatically, they are still chained to Yahoo. These users can be counted on for visiting the Yahoo web-properties,  seeing banner ads chosen by Yahoo (in other words, generating advertising revenue for the company) or running the mobile app on their phones. It is not until email forwarding is operational or customers decide they can afford to abandon any messages sent to their old email address that they can fully sever their ties with Yahoo.

That raises the question: does Yahoo have any obligation to provide email forwarding for the lifetime of the account? After all there is some cost to operating an email service. The unstated, if not deliberately obscured, assumption is that users indirectly pay for “free email” with their attention and privacy, being subjected to advertising while providing the raw-material of clicks for the data mining required to fine-tune the delivery of those ads. Arguably users who are no longer visiting the website or seeing ads are not holding up their end of this implicit bargain. They represent a net negative to the cloud provider. However the economics of carrying such inactive users may shake out, major providers do not appear to have embraced the Yahoo logic. Both Gmail and allow forwarding messages to another address chosen by the user. That seeming generosity may have something to do with the relatively small number of users taking advantage of the feature, representing negligible cost to either service. While both MSFT and Google were implicated in the NSA PRISM program revealed by Edward Snowden, neither company has quite faced the type of persistent backlash Yahoo experienced over its own surveillance debacle, or for that matter the gradual but steady decline in the company’s fortunes over Mayer’s tenure.

In fact Google goes above and beyond mail forwarding. In 2011 the company introduced a feature called Takeout, as part of the aptly named “Data Liberation Front” project. It allows users to download data associated with their Google services. The list which has been expanding since its introduction now includes not only the usual suspects of email and Google Drive files, but also search history, location, images, notes, calendar and YouTube videos. This is an upfront commitment to customers that their data will not be held hostage at Google. In fact Takeout seems to go out of its way to play well with rivals: it has an option to upload the resulting massive archive to Dropbox or MSFT OneDrive.

Screen Shot 2016-12-12 at 22.10.10.png

Google Takeout results can be saved to competing services

The bad news is that the dynamics of competition for cloud services has shifted: dreaded switching costs and lock-in effects associated with old-school enterprise software arise in this space too. The good news is that cloud services can still voluntarily go out of their way to offer functionality that restores some semblance of the conventional wisdom: “Our competitors are just one click away.”


“Code is law” is not a law

(Reflections on Ethereum Classic and modes of regulation)

East Coast code vs West Coast code

When Lawrence Lessig published Code and Other Laws of Cyberspace in 1999, it became an instant classic on technology policy. In a handful of chapters it chronicled how technology, or West Coast code, functions as a force for regulating behavior along side its more visible old-school counterpart: regulation by law, or East Coast code; economics (tax & raise the costs → discourage consumption); historic social-norms enforced by peer pressure. “Code is law” became the cliff-notes version of that pattern. Today that thesis is increasingly interpreted to mean that code ought to be law. Most recently Ethereum Classic has grabbed that banner in arguing against intervention in the DAO theft. This is a good time to revisit the original source. The subtlety of Code’s argument has been lost in the new incarnation: Lessig was no cheerleader for this transformation where code increasingly takes the place of laws in regulating behavior.

Renegade chains: Ethereum and Ethereum Classic

Ethereum Classic grew out of a major schism in the crypto-currency community around what “contracts” stand for. To recap: a German start-up attempted to put together a crowd-sourced venture capital firm as a smart-contract, called The DAO for “decentralized autonomous organization.” This was no ordinary contract spelled out in English, not even the obscure dialect legal professionals speak. Instead it was expressed in the precise, formal language of computer code. The contract would live on the Ethereum blockchain, its structure in public-view, every step of its execution transparent for all to observe. This was a bold vision, raising over $130 million based on the Ether/USD exchange rate of the time. Perhaps too bold, it turns out: there was a critical bug in the contract logic, which allowed an attacker to waltz away with $60 million of those funds.

Crypto-currency space has a decidedly libertarian ideology. In some of the more extreme positions, these can veer towards conspiratorial view of the Federal Reserve and debasement of currency through centralized intervention. So it was understandable that the decision by the Ethereum Foundation— closest analog to a governing body/standard forum/Politburo in this purportedly decentralized system— to bail out the DAO proved controversial. After all, there was no defect in the Ethereum protocol itself. As a decentralized platform for executing smart-contracts expressed in computer code, the system performed exactly as advertised. Instead there was a bug in one specific contract out of thousands written to execute on that platform. Rather inconveniently that one contract happened to carry close to 10% of all Ether, the currency of the realm, in existence at the time. It might as well have been a textbook behavioral economics experiment to demonstrate how bailouts, crony capitalism and “too-big-to-fail” can emerge naturally even in decentralized systems. The solution was a hard-fork to rewrite history on the blockchain, undoing the theft by reversing those transactions exploiting the vulnerability.

Between a fork and a hard-place

“When you come to a fork in the road, take it.”Yogi Berra

A blockchain is the emergent consensus out of a distributed system containing thousands of individual nodes. If consensus breaks down and nodes disagree about the state of the world— which transactions are valid, the balance of funds in each account, who owns some debt etc.— there is no longer a single chain. Instead there are two or more chains, a “fork” that splits the system into incompatible fragments or parallel universes with different states: a payment has been accepted in one but never received in the other, or a debt has been paid in one chain only. Before Ethereum made forks into a regular pastime, they were dreaded and avoided at all costs. Blockchains are designed to quickly put them out of existence and “converge” back on a happy consensus. Very short-lived forks happen all the time: in a large distributed system it is  expected that not every far-flung node will be in-sync with everyone else. It is not uncommon in Bitcoin for competing miners to discover new blocks almost simultaneously, with each group proceeding to build on their own resulting in diverging chains. But the protocol corrects such disagreements with rules designed to anoint a single chain as the “winner” to survive and all others to quickly vanish, with no new blocks mined to extend them. This works well in practice because most forks are not deliberate. They are an accidental side-effect of decentralization and limits on the propagation of information in a distributed system. Occasionally forks may even be introduced by bugs- Bitcoin experienced one in 2013 when nodes running an older version of the software started rejecting blocks deemed valid by the newer version.

Until recently it was unusual for a fork to be introduced deliberately. Bitcoin Core team in particular adopted a fork-averse philosophy, even if it means foregoing the opportunity to quickly evolve the protocol by forcing upgrades across the board with a threatened deadline. Such a game-of-chicken is exactly what the Ethereum Foundation proposed to undo the theft of funds from the DAO. Updated versions Ethereum software would disregard specific transactions implicated in the heist, in effect rewriting “history” on the blockchain to revert those funds back to their rightful owner. It’s as if Intel, the manufacturer that makes x86 processors that power most consumer PCs, decided to redesign their perfectly good hardware in order to work around a Windows bug because Windows was the most popular operating system running on x86. (Alternatively: some critics pointed to a conflict of interest in Ethereum Foundation members having personal stakes in the DAO. The analogy becomes Intel redesigning its chips in order to compensate for Windows bugs if Intel were an investor in Microsoft.)

Not everyone agreed this was a good idea. Ethereum Classic was the name adopted by the splinter faction refusing to go along with the fait accompli. Instead this group opted to run the previous version of the Ethereum software which continued to build a blockchain on existing, unaltered history. Ethereum Foundation initially did not pay much attention to the opposition. It was assumed that the situation would resolve itself just like naturally occurring forks: one chain emerging 100% victorious and the other one dying out completely, with all miners working on the winning chain. That’s not quite how reality played out, and in hindsight this should have been expected, given the material difference in intent between accidental forks arising intrinsically from decentralization vs deliberate forks introduced by fiat. Ethereum Classic (“ETC”) retained close to 10% the hash-rate of mainline Ethereum. It also achieved a valuation around 10% and became a liquid currency in its own right once the exchange Poloniex listed ETC for trading.

The dust may have settled after the hard-fork but the wisdom of bailing-out the DAO remains a highly divisive topic in the cryptocurrency space. Recently ETC proponents have rallied around an old idea: Code Is Law. According to this line of argument, the DAO contract was faithfully executed on the blockchain exactly as written. Everything that transpired, from the initial fund-raising to the eventual theft and desperate “Robin Hood” recovery attempts, proceeded according to terms specified out in the original contract. If the Ethereum system performed as advertised in enforcing terms of the contract,what justification can there be for resorting to this deus ex machina to override those terms? If Code is Law as Lessig decreed, DAO hard-fork constitutes an “unlawful” intervention in a financial system built around contracts by virtue of violating contractual terms expressed in code:

Code is law on the blockchain. In the sense, all executions and transactions are final and immutable. So, from our (Ethereum Classic supporters) standpoint by pushing the DAO hard fork EF broke the “law” in the sense that they imposed an invalid transaction state on the blockchain.

Code: benevolent and malicious

This is where revisiting “Code” is helpful. Lessig was by no means indifferent to the ways code, or architecture of physical space before there were computers, had been leveraged in the past to achieve political ends. One examples cited in the book are the bridges leading to Long Island: they were built too low for buses to pass, deterring minorities dependent on public transportation. Even in that unenlightened time, there were no overtly discriminatory laws on the books saying outright that African-Americans could not visit Long Island. Instead it was left up to the “code” road infrastructure to implement that disgraceful policy. Code may have supplanted law in this example but it was clearly not the right outcome.

In fact much of “Code” is a check on the unbridled optimism of the late 1990s when it was fashionable to portray the Internet as an unambiguous force for good: more avenues for self-expression, greater freedom of speech, improved privacy for communication through strong cryptography, an environment inhospitable to surveillance. In short, more of the good stuff everyone wants. More importantly the prevailing opinion held that this was the “manifest destiny” of the Internet. The Internet could not help but propel us closer towards to this happy outcome because it was somehow “designed” to increase personal freedom, defend privacy and combat censorship.

That view sounds downright naive this day and age of the Great Firewall of China, locked-down appliances chronicled in The Future of the Internet, state-sponsored disinformation campaigns and NSA mass-surveillance. But Lessig was prescient in sounding the alarm at the height of dot-com euphoria: “Code” spoke of architectures of control as well as architectures of freedom as being equal possibilities for the future. When the process of public legislation, however dysfunctional and messy it may be, is supplanted by private agendas baked into software, there is no guarantee that the outcome will align with the values associated with the Internet in its early years. There is no assurance that a future update to code running the infrastructure will not nudge the Internet towards becoming a platform for mass consumer surveillance, walled-gardens, echo-chambers, invisible censorship and subtle manipulation.

Hard-forks as deus ex machina

There is much to be said about not making random edits to blockchains when the intervention can not be justified on technical merits. It’s one thing to change the rules of the game to implement some critical security improvement, as Ethereum recently did to improve resilience against DDoS attacks. This time there were no splinter-cells taking up the banner of the abandoned chain. By contrast, Ethereum Foundation actively cheerleading the controversial DAO hard-fork opens Pandora’s box: here is proof that blockchain interventions can be orchestrated on demand by a centralized group, even in a purportedly decentralized system that was supposed to be at the whim of its own users. What prevents repressive regimes from asking the Foundation to block funds sent to political dissidents in a future update? Could a litigious enterprise with creative lawyers take the Foundation to court over some transaction they would like to see reversed?

These questions are only scratching the surface. Many valid arguments can be advanced in favor of or in opposition to the DAO hard-fork. It is not the intent of this blog post to spill more electrons on that debate. The meta-point is that such complexity can not be dismissed with a simplistic appeal to “code-is-law,” however appealing such slogans may be. Lessig’s original observation was descriptive— an observation about how the architecture of the Internet is being used to supplant or subvert existing regulation. Ethereum Classic misappropriates that into a normative statement: code should be law and this an unadulterated good.

Comparing this notion of “law” to physical laws such as gravity is misleading. One does not have any choice in following the laws of nature; Mother Nature neither requires no policing to enforce her rules nor has need to mete out punishment for transgressions. By contrast, laws in a free society represent a voluntary bargain members of that society have collectively agreed to. They are effective only to the extent that such agreements are honored nearly universally and vigorously enforced against the few who run afoul of them. The consensus in that agreement can change over time. Unjust laws can be challenged through democratic channels. With sufficient support they can be changed. At one point several states in the US had anti-miscegenation laws on the books. Today such discrimination would be considered unthinkable. “Code is law” in the Ethereum Classic sense represents not  an inescapable fact of life as a deliberate choice to cede control over enforcement of contracts to pieces of code executed on a blockchain. That choice is not a death pact. Code itself may have no room for ambiguity in its logic, but what lends power to that code is the voluntary decision by blockchain participants to prop up the platform it executes on. The validity of that platform can be challenged and its rules modified by consensus. In fact every hard-fork is in effect changing the rules of the game on a blockchain: some contract that used to be valid under the previous design is invalidated.

Not all instances of West Coast code supplanting East Coast code are beneficial or desirable from a social standpoint. Blind adherence to the primacy of West Coast code is unlikely to yield an acceptable alternative to contract law.


(Edited 11/06: fixed incomplete sentence in the first paragraph, added clarification about   ideological positions)

Use and misuse of code-signing (part II)

[continued from part I]

There is no “evil-bit”

X509 certificates represent assertions about identity. They are not assertions about competence, good intentions, code-quality or sound software engineering practices. Code-signing solutions including Authenticode can only communicate information about the identity of the software publisher— the answer to the question: “who authored this piece of software?” That is the raison d’etre for the existence of certificate authorities and why they ostensibly charge hefty sums for their services. When developer Alice wants to obtain a code-signing certificate with her name on it, the CA must perform due diligence that it is really Alice requesting the certificate. Because if an Alice certificate is mistakenly issued to Bob, suddenly applications written by Bob will be incorrectly attributed to Alice, unfairly using her reputation and in the process quite possibly tarnishing that reputation. In the real world, code-signing certificates are typically issued not to individuals toiling alone— although many independent developers have obtained one for personal use— but large companies with hundreds or thousands of engineers. But the principle is same: a code-signing certificate for MSFT must not be given willy-nilly to random strangers who are not affiliated with MSFT. (Incidentally that exact scenario was one of the early debacles witnessed in the checkered history of public CAs.)

Nothing in this scheme vouches for the integrity of the software publisher or the fairness of their business model. CAs are only asserting that they have carefully verified the identity of the developer prior to issuing the certificate. Whether or not the software signed by that developer is “good” or suitable for any particular purpose is outside the scope of that statement. In that sense, there is nothing wrong— as far as X509 is concerned— with a perfectly valid digital certificate signing malicious code. There is no evil bit required in a digital certificate for publishers planning to ship malware. For that matter there is no “competent bit” to indicate that software published by otherwise well-meaning developers will not cause harm nevertheless due to inadvertent bugs or dangerous vulnerabilities. (Otherwise no one could issue certificates to Adobe.)

1990s called, they want their trust-model back

This observation is by no means novel or new. Very early on in the development of Authenticode in 1997, a developer made this point loud and clear. He obtained a valid digital certificate from Verisign and used it to sign an ActiveX control dubbed “Internet Exploder” [sic] designed to shut-down a machine when it was embedded on a web page. That particular payload was innocuous and at best a minor nuisance, but the message was unambiguous: the same signed ActiveX control could have reformatted the drive or steal information. “Signed” does not equal “trustworthy.”

Chalk it up to the naivete of the 1990s. One imagines a program manager at MSFT arguing this is good enough: “Surely no criminal will be foolish enough to self-incriminate by signing malware with their own company identity?” Yet a decade later that exact scenario is observed in the wild. What went wrong? The missing ingredient is deterrence. There is no global malware-police to chase after every malware outfit even when they are operating brazenly in the open, leaving a digitally authenticated trail of evidence in their wake. Requiring everyone to wear identity badges only creates meaningful deterrence  when there are consequences to being caught engaging in criminal activity while flashing those badges.

Confusing authentication and trust

Confusing authentication with authorization is a common mistake in information security. It is particularly tempting to blur the line when authorization can be revoked by deliberately failing authentication. A signed ActiveX control is causing potential harm to users? Let’s revoke the certificate and that signature will no longer verify. This conceptual shortcut is often a sign that a system lacks proper authorization design: when the only choices are binary yes/no, one resorts to denying authorization by blocking authentication.

Developer identity is neither necessary or sufficient for establishing trust. It is not necessary because there is plenty of perfectly useful open-source software maintained by talent developers only known by their Github handle, without direct attribution of each line of code to a person identified by their legal name. It is not sufficient either, because knowing that some application was authored by Bob is not useful on its own, unless one has additional information about Bob’s qualifications as a software publisher. In other words: reputation. In the absence of any information about Bob, there is no way to decide if he is a fly-by-night spyware operation or honest developer with years of experience shipping quality code.

Certificate authorities as reluctant malware-police

Interesting enough, that 1997 incident set another precedent: Verisign responded by revoking the certificate, alleging that signing this deliberately harmful ActiveX control was a violation of the certificate policy that this software developer agreed to as a condition for issuance. Putting aside the enforceability of TOUs and click-through agreements, this is a downright unrealistic demand for certificate authorities to start policing developers on questions of policy completely unrelated to verifying their identity. It’s as if the DMV had been tasked with revoking driver’s licenses for people who are late on their credit-card payments.

That also explains why revoking certificates for a misbehaving vendors is not an effective way to stop that developer from churning out malware. As the paper points out, there are many ways to game the system, all of which being used in the wild by companies with a track record of publishing harmful applications:

  • CA shopping: after being booted from one CA, simply walk over to their competitor to get another certificate for the exact same corporate entity
  • Cosmetic changes: get certificates for the same company with slightly modified information (eg variant of address or company name) from the same CA
  • Starting over: create a different shell-company doing exactly same line of business to start with a clean-slate

In effect CAs are playing whack-a-mole with malware authors, something they are neither qualifier or motivated to do. In the absence of a reputation system, the ecosystem is stuck with a model where revoking trust in malicious code requires revoking the identity of the author. This is a very different use of revocation than what the X509 standard envisioned. Here are the possible reasons defined in the specification– incidentally these appear in the published revocation status:


unspecified             (0),
 keyCompromise           (1),
 cACompromise            (2),
 affiliationChanged      (3),
 superseded              (4),
 cessationOfOperation    (5),
 certificateHold         (6),
 -- value 7 is not used
 removeFromCRL           (8),
 privilegeWithdrawn      (9),
 aACompromise           (10) }

Note there is no option called “published malicious application.” That’s because none of the assertions made by the CA are invalidated upon discovering that a software publisher is churning out malware. Compare that to key-compromise (reason #1 above) where the private-key of the publisher has been obtained by an attacker. In that case a critical assertion has been voided: the public-key appearing in the certificate no longer speaks exclusively for the certificate holder. Similarly a change of affiliation could arise when an employee leaves a company, a certificate issued in the past now contains inaccurate information for “organization” and “organizational unit” fields. There is no analog for the discovery of signed malware, other than vague reference to compliance with the certificate policy. (In fairness, the policy itself can appear as URL in the certificate but it requires careful legal analysis to answer the question of how exactly the certificate subject has diverged from that policy.)

Code-signing is not the only area where this mission creep has occurred but it is arguably the one where highest demands are put on the actors least capable of fulfilling those expectations. Compare this to issuance of certificates for SSL: when phishing websites pop-up impersonating popular services, perhaps with a subtle misspelling of the name, complete with a valid SSL certificate. Here there may be valid legal grounds to ask the responsible CA to revoke a certificate because there may be trademark claim. (Not that it does any good, since the implementation of revocation in popular browsers ranges from half-hearted to comically flawed.) Lukcily web browsers have other ways to stop users from visiting harmful websites: for example, Safe Browsing and SmartScreen maintain blacklists of malicious pages. There is no reason to wait for CA to take any action- and for malicious sites that are not using SSL, it would not be possible anyway.

Code-signing presents a different problem. In open software ecosystems, reputation systems are rudimentary. Antivirus applications can recognize specific instances of malware but most applications start from a presumption of innocence. In the absence of other contextual clues, the mere existence of verifiable developer identity becomes a proxy for trust decision: unsigned applications are suspect, signed ones get a free pass. At least, until it becomes evident that the signed application was harmful. At that point, the most reliable way of withdrawing trust is to invalidate signatures by revoking the certificate. This uphill battle requires enlisting CAs in a game of whack-a-mole, even when they performed their job correctly in the first place.

This problem is unique to open models for software distribution, where applications can be sourced from anywhere on the web. By contrast, the type of tightly controlled “walled-garden” ecosystem Apple favors with its own App Store rarely has to worry about revoking anything, even though it may use code signing. If Apple deems an application harmful, it can be simply yanked from the store. (For that matter, since Apple has remote control over devices in the field, they can also uninstall existing copies from users’ devices.)

Reputation systems can solve this problem without resorting to restrictive walled-gardens or locking down application distribution to a single centralized service responsible for quality. They would also take CAs out of policing miscreants, a job they are uniquely ill-suited for. In order to block software published by Bob, it is not necessary to revoke Bob’s certificate. It is sufficient instead to signal a very low reputation for Bob. This also moves the conflict one-level higher, because reputations are attached to persons or companies, not to specific certificates. Getting more certificates from another CA after one has been revoked does not help Bob. As long as the reputation system can correlate the identities involved, the dismal reputation will follow Bob. Instead of asking CAs to reject customers who had certificates revoked from a different CA, the reputation system allows CAs do their job and focus on their core business: vet the identity of certificate subjects. It is up to the reputation system to link different certificates based on a common identity, or even related families of malware published by seemingly distinct entities acting on behalf of the same malware shop.