Entries Tagged "random numbers"

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CAs Reissue Over One Million Weak Certificates

Turns out that the software a bunch of CAs used to generate public-key certificates was flawed: they created random serial numbers with only 63 bits instead of the required 64. That may not seem like a big deal to the layman, but that one bit change means that the serial numbers only have half the required entropy. This really isn’t a security problem; the serial numbers are to protect against attacks that involve weak hash functions, and we don’t allow those weak hash functions anymore. Still, it’s a good thing that the CAs are reissuing the certificates. The point of a standard is that it’s to be followed.

Posted on March 18, 2019 at 6:23 AM • View Comments

Proof that HMAC-DRBG has No Back Doors

New research: “Verified Correctness and Security of mbedTLS HMAC-DRBG,” by Katherine Q. Ye, Matthew Green, Naphat Sanguansin, Lennart Beringer, Adam Petcher, and Andrew W. Appel.

Abstract: We have formalized the functional specification of HMAC-DRBG (NIST 800-90A), and we have proved its cryptographic security—that its output is pseudorandom—using a hybrid game-based proof. We have also proved that the mbedTLS implementation (C program) correctly implements this functional specification. That proof composes with an existing C compiler correctness proof to guarantee, end-to-end, that the machine language program gives strong pseudorandomness. All proofs (hybrid games, C program verification, compiler, and their composition) are machine-checked in the Coq proof assistant. Our proofs are modular: the hybrid game proof holds on any implementation of HMAC-DRBG that satisfies our functional specification. Therefore, our functional specification can serve as a high-assurance reference.

Posted on August 30, 2017 at 6:37 AM • View Comments

Hacking Slot Machines by Reverse-Engineering the Random Number Generators

Interesting story:

The venture is built on Alex’s talent for reverse engineering the algorithms—known as pseudorandom number generators, or PRNGs—that govern how slot machine games behave. Armed with this knowledge, he can predict when certain games are likeliest to spit out money­insight that he shares with a legion of field agents who do the organization’s grunt work.

These agents roam casinos from Poland to Macau to Peru in search of slots whose PRNGs have been deciphered by Alex. They use phones to record video of a vulnerable machine in action, then transmit the footage to an office in St. Petersburg. There, Alex and his assistants analyze the video to determine when the games’ odds will briefly tilt against the house. They then send timing data to a custom app on an agent’s phone; this data causes the phones to vibrate a split second before the agent should press the “Spin” button. By using these cues to beat slots in multiple casinos, a four-person team can earn more than $250,000 a week.

It’s an interesting article; I have no idea how much of it is true.

The sad part is that the slot-machine vulnerability is so easy to fix. Although the article says that “writing such algorithms requires tremendous mathematical skill,” it’s really only true that designing the algorithms requires that skill. Using any secure encryption algorithm or hash function as a PRNG is trivially easy. And there’s no reason why the system can’t be designed with a real RNG. There is some randomness in the system somewhere, and it can be added into the mix as well. The programmers can use a well-designed algorithm, like my own Fortuna, but even something less well-thought-out is likely to foil this attack.

Posted on August 7, 2017 at 6:00 AM • View Comments

Predicting a Slot Machine's PRNG

Wired is reporting on a new slot machine hack. A Russian group has reverse-engineered a particular brand of slot machine—from Austrian company Novomatic—and can simulate and predict the pseudo-random number generator.

The cell phones from Pechanga, combined with intelligence from investigations in Missouri and Europe, revealed key details. According to Willy Allison, a Las Vegas­-based casino security consultant who has been tracking the Russian scam for years, the operatives use their phones to record about two dozen spins on a game they aim to cheat. They upload that footage to a technical staff in St. Petersburg, who analyze the video and calculate the machine’s pattern based on what they know about the model’s pseudorandom number generator. Finally, the St. Petersburg team transmits a list of timing markers to a custom app on the operative’s phone; those markers cause the handset to vibrate roughly 0.25 seconds before the operative should press the spin button.

“The normal reaction time for a human is about a quarter of a second, which is why they do that,” says Allison, who is also the founder of the annual World Game Protection Conference. The timed spins are not always successful, but they result in far more payouts than a machine normally awards: Individual scammers typically win more than $10,000 per day. (Allison notes that those operatives try to keep their winnings on each machine to less than $1,000, to avoid arousing suspicion.) A four-person team working multiple casinos can earn upwards of $250,000 in a single week.

The easy solution is to use a random-number generator that accepts local entropy, like Fortuna. But there’s probably no way to easily reprogram those old machines.

Posted on February 8, 2017 at 6:48 AM • View Comments

Hacking Lottery Machines

Interesting article about how a former security director of the US Multi-State Lottery Association hacked the random-number generator in lottery software so he could predict the winning numbers.

For several years, Eddie Tipton, the former security director of the US Multi-State Lottery Association, installed software code that allowed him to predict winning numbers on specific days of the year, investigators allege. The random-number generators had been erased, but new forensic evidence has revealed how the hack was apparently done.

[…]

The number generator had apparently been hacked to produce predictable numbers on three days of the year, after the machine had gone through a security audit.

Note that last bit. The software would only produce the non-random results after the software security audit was completed.

It’s getting harder and harder to trust opaque and unaccountable algorithms. Anyone who thinks we should have electronic voting machines—or worse, Internet voting—needs to pay attention.

Posted on April 12, 2016 at 6:39 AM • View Comments

Back Door in Juniper Firewalls

Juniper has warned about a malicious back door in its firewalls that automatically decrypts VPN traffic. It’s been there for years.

Hopefully details are forthcoming, but the folks at Hacker News have pointed to this page about Juniper’s use of the DUAL_EC_DBRG random number generator. For those who don’t immediately recognize that name, it’s the pseudo-random-number generator that was backdoored by the NSA. Basically, the PRNG uses two secret parameters to create a public parameter, and anyone who knows those secret parameters can predict the output. In the standard, the NSA chose those parameters. Juniper doesn’t use those tainted parameters. Instead:

ScreenOS does make use of the Dual_EC_DRBG standard, but is designed to not use Dual_EC_DRBG as its primary random number generator. ScreenOS uses it in a way that should not be vulnerable to the possible issue that has been brought to light. Instead of using the NIST recommended curve points it uses self-generated basis points and then takes the output as an input to FIPS/ANSI X.9.31 PRNG, which is the random number generator used in ScreenOS cryptographic operations.

This means that all anyone has to do to break the PRNG is to hack into the firewall and copy or modify those “self-generated basis points.”

Here’s a good summary of what we know. The conclusion:

Again, assuming this hypothesis is correct then, if it wasn’t the NSA who did this, we have a case where a US government backdoor effort (Dual-EC) laid the groundwork for someone else to attack US interests. Certainly this attack would be a lot easier given the presence of a backdoor-friendly RNG already in place. And I’ve not even discussed the SSH backdoor which, as Wired notes, could have been the work of a different group entirely. That backdoor certainly isn’t NOBUS—Fox-IT claim to have found the backdoor password in six hours.

More details to come, I’m sure.

EDITED TO ADD (12/21): A technical overview of the SSH backdoor.

EDITED TO ADD (12/22): Matthew Green wrote a really good technical post about this.

They then piggybacked on top of it to build a backdoor of their own, something they were able to do because all of the hard work had already been done for them. The end result was a period in which someone—maybe a foreign government—was able to decrypt Juniper traffic in the U.S. and around the world. And all because Juniper had already paved the road.

Another good article.

Posted on December 21, 2015 at 6:52 AM • View Comments

Risks of Not Understanding a One-Way Function

New York City officials anonymized license plate data by hashing the individual plate numbers with MD5. (I know, they shouldn’t have used MD5, but ignore that for a moment.) Because they didn’t attach long random strings to the plate numbers—i.e., salt—it was trivially easy to hash all valid license plate numbers and deanonymize all the data.

Of course, this technique is not news.

ArsTechnica article. Hacker News thread.

Posted on June 25, 2014 at 6:36 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.