September 15, 2019
by Bruce Schneier
Fellow and Lecturer, Harvard Kennedy School
A free monthly newsletter providing summaries, analyses, insights, and commentaries on security: computer and otherwise.
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- Bypassing Apple FaceID’s Liveness Detection Feature
- Software Vulnerabilities in the Boeing 787
- Influence Operations Kill Chain
- Surveillance as a Condition for Humanitarian Aid
- Google Finds 20-Year-Old Microsoft Windows Vulnerability
- Modifying a Tesla to Become a Surveillance Platform
- License Plate “NULL”
- Detecting Credit Card Skimmers
- The Threat of Fake Academic Research
- The Myth of Consumer-Grade Security
- AI Emotion-Detection Arms Race
- Attacking the Intel Secure Enclave
- Massive iPhone Hack Targets Uyghurs
- Credit Card Privacy
- The Doghouse: Crown Sterling
- Default Password for GPS Trackers
- On Cybersecurity Insurance
- More on Law Enforcement Backdoor Demands
- Fabricated Voice Used in Financial Fraud
- Smart Watches and Cheating on Tests
- When Biology Becomes Software
- Upcoming Speaking Engagements
[2019.08.15] Apple’s FaceID has a liveness detection feature, which prevents someone from unlocking a victim’s phone by putting it in front of his face while he’s sleeping. That feature has been hacked:
Researchers on Wednesday during Black Hat USA 2019 demonstrated an attack that allowed them to bypass a victim’s FaceID and log into their phone simply by putting a pair of modified glasses on their face. By merely placing tape carefully over the lenses of a pair glasses and placing them on the victim’s face the researchers demonstrated how they could bypass Apple’s FaceID in a specific scenario. The attack itself is difficult, given the bad actor would need to figure out how to put the glasses on an unconscious victim without waking them up.
At the Black Hat security conference today in Las Vegas, Santamarta, a researcher for security firm IOActive, plans to present his findings, including the details of multiple serious security flaws in the code for a component of the 787 known as a Crew Information Service/Maintenance System. The CIS/MS is responsible for applications like maintenance systems and the so-called electronic flight bag, a collection of navigation documents and manuals used by pilots. Santamarta says he found a slew of memory corruption vulnerabilities in that CIS/MS, and he claims that a hacker could use those flaws as a foothold inside a restricted part of a plane’s network. An attacker could potentially pivot, Santamarta says, from the in-flight entertainment system to the CIS/MS to send commands to far more sensitive components that control the plane’s safety-critical systems, including its engine, brakes, and sensors. Boeing maintains that other security barriers in the 787’s network architecture would make that progression impossible.
Santamarta admits that he doesn’t have enough visibility into the 787’s internals to know if those security barriers are circumventable. But he says his research nonetheless represents a significant step toward showing the possibility of an actual plane-hacking technique. “We don’t have a 787 to test, so we can’t assess the impact,” Santamarta says. “We’re not saying it’s doomsday, or that we can take a plane down. But we can say: This shouldn’t happen.”
Boeing denies that there’s any problem:
In a statement, Boeing said it had investigated IOActive’s claims and concluded that they don’t represent any real threat of a cyberattack. “IOActive’s scenarios cannot affect any critical or essential airplane system and do not describe a way for remote attackers to access important 787 systems like the avionics system,” the company’s statement reads. “IOActive reviewed only one part of the 787 network using rudimentary tools, and had no access to the larger system or working environments. IOActive chose to ignore our verified results and limitations in its research, and instead made provocative statements as if they had access to and analyzed the working system. While we appreciate responsible engagement from independent cybersecurity researchers, we’re disappointed in IOActive’s irresponsible presentation.”
This being Black Hat and Las Vegas, I’ll say it this way: I would bet money that Boeing is wrong. I don’t have an opinion about whether or not it’s lying.
[2019.08.19] Influence operations are elusive to define. The Rand Corp.’s definition is as good as any: “the collection of tactical information about an adversary as well as the dissemination of propaganda in pursuit of a competitive advantage over an opponent.” Basically, we know it when we see it, from bots controlled by the Russian Internet Research Agency to Saudi attempts to plant fake stories and manipulate political debate. These operations have been run by Iran against the United States, Russia against Ukraine, China against Taiwan, and probably lots more besides.
Since the 2016 US presidential election, there have been an endless series of ideas about how countries can defend themselves. It’s time to pull those together into a comprehensive approach to defending the public sphere and the institutions of democracy.
Influence operations don’t come out of nowhere. They exploit a series of predictable weaknesses—and fixing those holes should be the first step in fighting them. In cybersecurity, this is known as a “kill chain.” That can work in fighting influence operations, too—laying out the steps of an attack and building the taxonomy of countermeasures.
In an exploratory blog post, I first laid out a straw man information operations kill chain. I started with the seven commandments, or steps, laid out in a 2018 New York Times opinion video series on “Operation Infektion,” a 1980s Russian disinformation campaign. The information landscape has changed since the 1980s, and these operations have changed as well. Based on my own research and feedback from that initial attempt, I have modified those steps to bring them into the present day. I have also changed the name from “information operations” to “influence operations,” because the former is traditionally defined by the US Department of Defense in ways that don’t really suit these sorts of attacks.
Step 1: Find the cracks in the fabric of society—the social, demographic, economic, and ethnic divisions. For campaigns that just try to weaken collective trust in government’s institutions, lots of cracks will do. But for influence operations that are more directly focused on a particular policy outcome, only those related to that issue will be effective.
Countermeasures: There will always be open disagreements in a democratic society, but one defense is to shore up the institutions that make that society possible. Elsewhere I have written about the “common political knowledge” necessary for democracies to function. That shared knowledge has to be strengthened, thereby making it harder to exploit the inevitable cracks. It needs to be made unacceptable—or at least costly—for domestic actors to use these same disinformation techniques in their own rhetoric and political maneuvering, and to highlight and encourage cooperation when politicians honestly work across party lines. The public must learn to become reflexively suspicious of information that makes them angry at fellow citizens. These cracks can’t be entirely sealed, as they emerge from the diversity that makes democracies strong, but they can be made harder to exploit. Much of the work in “norms” falls here, although this is essentially an unfixable problem. This makes the countermeasures in the later steps even more important.
Step 2: Build audiences, either by directly controlling a platform (like RT) or by cultivating relationships with people who will be receptive to those narratives. In 2016, this consisted of creating social media accounts run either by human operatives or automatically by bots, making them seem legitimate, gathering followers. In the years following, this has gotten subtler. As social media companies have gotten better at deleting these accounts, two separate tactics have emerged. The first is microtargeting, where influence accounts join existing social circles and only engage with a few different people. The other is influencer influencing, where these accounts only try to affect a few proxies (see step 6)—either journalists or other influencers—who can carry their message for them.
Countermeasures: This is where social media companies have made all the difference. By allowing groups of like-minded people to find and talk to each other, these companies have given propagandists the ability to find audiences who are receptive to their messages. Social media companies need to detect and delete accounts belonging to propagandists as well as bots and groups run by those propagandists. Troll farms exhibit particular behaviors that the platforms need to be able to recognize. It would be best to delete accounts early, before those accounts have the time to establish themselves.
This might involve normally competitive companies working together, since operations and account names often cross platforms, and cross-platform visibility is an important tool for identifying them. Taking down accounts as early as possible is important, because it takes time to establish the legitimacy and reach of any one account. The NSA and US Cyber Command worked with the FBI and social media companies to take down Russian propaganda accounts during the 2018 midterm elections. It may be necessary to pass laws requiring Internet companies to do this. While many social networking companies have reversed their “we don’t care” attitudes since the 2016 election, there’s no guarantee that they will continue to remove these accounts—especially since their profits depend on engagement and not accuracy.
Step 3: Seed distortion by creating alternative narratives. In the 1980s, this was a single “big lie,” but today it is more about many contradictory alternative truths—a “firehose of falsehood“—that distort the political debate. These can be fake or heavily slanted news stories, extremist blog posts, fake stories on real-looking websites, deepfake videos, and so on.
Countermeasures: Fake news and propaganda are viruses; they spread through otherwise healthy populations. Fake news has to be identified and labeled as such by social media companies and others, including recognizing and identifying manipulated videos known as deepfakes. Facebook is already making moves in this direction. Educators need to teach better digital literacy, as Finland is doing. All of this will help people recognize propaganda campaigns when they occur, so they can inoculate themselves against their effects. This alone cannot solve the problem, as much sharing of fake news is about social signaling, and those who share it care more about how it demonstrates their core beliefs than whether or not it is true. Still, it is part of the solution.
Step 4: Wrap those narratives in kernels of truth. A core of fact makes falsehoods more believable and helps them spread. Releasing stolen emails from Hillary Clinton’s campaign chairman John Podesta and the Democratic National Committee, or documents from Emmanuel Macron’s campaign in France, were both an example of that kernel of truth. Releasing stolen emails with a few deliberate falsehoods embedded among them is an even more effective tactic.
Countermeasures: Defenses involve exposing the untruths and distortions, but this is also complicated to put into practice. Fake news sows confusion just by being there. Psychologists have demonstrated that an inadvertent effect of debunking a piece of fake news is to amplify the message of that debunked story. Hence, it is essential to replace the fake news with accurate narratives that counter the propaganda. That kernel of truth is part of a larger true narrative. The media needs to learn skepticism about the chain of information and to exercise caution in how they approach debunked stories.
Step 5: Conceal your hand. Make it seem as if the stories came from somewhere else.
Countermeasures: Here the answer is attribution, attribution, attribution. The quicker an influence operation can be pinned on an attacker, the easier it is to defend against it. This will require efforts by both the social media platforms and the intelligence community, not just to detect influence operations and expose them but also to be able to attribute attacks. Social media companies need to be more transparent about how their algorithms work and make source publications more obvious for online articles. Even small measures like the Honest Ads Act, requiring transparency in online political ads, will help. Where companies lack business incentives to do this, regulation will be the only answer.
Step 6: Cultivate proxies who believe and amplify the narratives. Traditionally, these people have been called “useful idiots.” Encourage them to take action outside of the Internet, like holding political rallies, and to adopt positions even more extreme than they would otherwise.
Countermeasures: We can mitigate the influence of people who disseminate harmful information, even if they are unaware they are amplifying deliberate propaganda. This does not mean that the government needs to regulate speech; corporate platforms already employ a variety of systems to amplify and diminish particular speakers and messages. Additionally, the antidote to the ignorant people who repeat and amplify propaganda messages is other influencers who respond with the truth—in the words of one report, we must “make the truth louder.” Of course, there will always be true believers for whom no amount of fact-checking or counter-speech will suffice; this is not intended for them. Focus instead on persuading the persuadable.
Step 7: Deny involvement in the propaganda campaign, even if the truth is obvious. Although since one major goal is to convince people that nothing can be trusted, rumors of involvement can be beneficial. The first was Russia’s tactic during the 2016 US presidential election; it employed the second during the 2018 midterm elections.
Countermeasures: When attack attribution relies on secret evidence, it is easy for the attacker to deny involvement. Public attribution of information attacks must be accompanied by convincing evidence. This will be difficult when attribution involves classified intelligence information, but there is no alternative. Trusting the government without evidence, as the NSA’s Rob Joyce recommended in a 2016 talk, is not enough. Governments will have to disclose.
Step 8: Play the long game. Strive for long-term impact over immediate effects. Engage in multiple operations; most won’t be successful, but some will.
Countermeasures: Counterattacks can disrupt the attacker’s ability to maintain influence operations, as US Cyber Command did during the 2018 midterm elections. The NSA’s new policy of “persistent engagement” (see the article by, and interview with, US Cyber Command Commander Paul Nakasone here) is a strategy to achieve this. So are targeted sanctions and indicting individuals involved in these operations. While there is little hope of bringing them to the United States to stand trial, the possibility of not being able to travel internationally for fear of being arrested will lead some people to refuse to do this kind of work. More generally, we need to better encourage both politicians and social media companies to think beyond the next election cycle or quarterly earnings report.
Permeating all of this is the importance of deterrence. Deterring them will require a different theory. It will require, as the political scientist Henry Farrell and I have postulated, thinking of democracy itself as an information system and understanding “Democracy’s Dilemma“: how the very tools of a free and open society can be subverted to attack that society. We need to adjust our theories of deterrence to the realities of the information age and the democratization of attackers. If we can mitigate the effectiveness of influence operations, if we can publicly attribute, if we can respond either diplomatically or otherwise—we can deter these attacks from nation-states.
None of these defensive actions is sufficient on its own. Steps overlap and in some cases can be skipped. Steps can be conducted simultaneously or out of order. A single operation can span multiple targets or be an amalgamation of multiple attacks by multiple actors. Unlike a cyberattack, disrupting will require more than disrupting any particular step. It will require a coordinated effort between government, Internet platforms, the media, and others.
Also, this model is not static, of course. Influence operations have already evolved since the 2016 election and will continue to evolve over time—especially as countermeasures are deployed and attackers figure out how to evade them. We need to be prepared for wholly different kinds of influencer operations during the 2020 US presidential election. The goal of this kill chain is to be general enough to encompass a panoply of tactics but specific enough to illuminate countermeasures. But even if this particular model doesn’t fit every influence operation, it’s important to start somewhere.
Others have worked on similar ideas. Anthony Soules, a former NSA employee who now leads cybersecurity strategy for Amgen, presented this concept at a private event. Clint Watts of the Alliance for Securing Democracy is thinking along these lines as well. The Credibility Coalition’s Misinfosec Working Group proposed a “misinformation pyramid.” The US Justice Department developed a “Malign Foreign Influence Campaign Cycle,” with associated countermeasures.
The threat from influence operations is real and important, and it deserves more study. At the same time, there’s no reason to panic. Just as overly optimistic technologists were wrong that the Internet was the single technology that was going to overthrow dictators and liberate the planet, so pessimists are also probably wrong that it is going to empower dictators and destroy democracy. If we deploy countermeasures across the entire kill chain, we can defend ourselves from these attacks.
But Russian interference in the 2016 presidential election shows not just that such actions are possible but also that they’re surprisingly inexpensive to run. As these tactics continue to be democratized, more people will attempt them. And as more people, and multiple parties, conduct influence operations, they will increasingly be seen as how the game of politics is played in the information age. This means that the line will increasingly blur between influence operations and politics as usual, and that domestic influencers will be using them as part of campaigning. Defending democracy against foreign influence also necessitates making our own political debate healthier.
This essay previously appeared in Foreign Policy.
Despite the best intentions, the decision to deploy technology like biometrics is built on a number of unproven assumptions, such as, technology solutions can fix deeply embedded political problems. And that auditing for fraud requires entire populations to be tracked using their personal data. And that experimental technologies will work as planned in a chaotic conflict setting. And last, that the ethics of consent don’t apply for people who are starving.
At the Defcon hacker conference today, security researcher Truman Kain debuted what he calls the Surveillance Detection Scout. The DIY computer fits into the middle console of a Tesla Model S or Model 3, plugs into its dashboard USB port, and turns the car’s built-in cameras—the same dash and rearview cameras providing a 360-degree view used for Tesla’s Autopilot and Sentry features—into a system that spots, tracks, and stores license plates and faces over time. The tool uses open source image recognition software to automatically put an alert on the Tesla’s display and the user’s phone if it repeatedly sees the same license plate. When the car is parked, it can track nearby faces to see which ones repeatedly appear. Kain says the intent is to offer a warning that someone might be preparing to steal the car, tamper with it, or break into the driver’s nearby home.
Although the initial $12,000-worth of fines were removed, the private company that administers the database didn’t fix the issue and new NULL tickets are still showing up.
The unanswered question is: now that he has a way to get parking fines removed, can he park anywhere for free?
And this isn’t the first time this sort of thing has happened. Wired has a roundup of people whose license places read things like “NOPLATE,” “NO TAG,” and “XXXXXXX.”
The team from the University of California San Diego, who worked with other computer scientists from the University of Illinois, developed an app called Bluetana which not only scans and detects Bluetooth signals, but can actually differentiate those coming from legitimate devices—like sensors, smartphones, or vehicle tracking hardware—from card skimmers that are using the wireless protocol as a way to harvest stolen data. The full details of what criteria Bluetana uses to differentiate the two isn’t being made public, but its algorithm takes into account metrics like signal strength and other telltale markers that were pulled from data based on scans made at 1,185 gas stations across six different states.
[2019.08.28] The Department of Justice wants access to encrypted consumer devices but promises not to infiltrate business products or affect critical infrastructure. Yet that’s not possible, because there is no longer any difference between those categories of devices. Consumer devices are critical infrastructure. They affect national security. And it would be foolish to weaken them, even at the request of law enforcement.
In his keynote address at the International Conference on Cybersecurity, Attorney General William Barr argued that companies should weaken encryption systems to gain access to consumer devices for criminal investigations. Barr repeated a common fallacy about a difference between military-grade encryption and consumer encryption: “After all, we are not talking about protecting the nation’s nuclear launch codes. Nor are we necessarily talking about the customized encryption used by large business enterprises to protect their operations. We are talking about consumer products and services such as messaging, smart phones, e-mail, and voice and data applications.”
The thing is, that distinction between military and consumer products largely doesn’t exist. All of those “consumer products” Barr wants access to are used by government officials—heads of state, legislators, judges, military commanders and everyone else—worldwide. They’re used by election officials, police at all levels, nuclear power plant operators, CEOs and human rights activists. They’re critical to national security as well as personal security.
This wasn’t true during much of the Cold War. Before the Internet revolution, military-grade electronics were different from consumer-grade. Military contracts drove innovation in many areas, and those sectors got the cool new stuff first. That started to change in the 1980s, when consumer electronics started to become the place where innovation happened. The military responded by creating a category of military hardware called COTS: commercial off-the-shelf technology. More consumer products became approved for military applications. Today, pretty much everything that doesn’t have to be hardened for battle is COTS and is the exact same product purchased by consumers. And a lot of battle-hardened technologies are the same computer hardware and software products as the commercial items, but in sturdier packaging.
Through the mid-1990s, there was a difference between military-grade encryption and consumer-grade encryption. Laws regulated encryption as a munition and limited what could legally be exported only to key lengths that were easily breakable. That changed with the rise of Internet commerce, because the needs of commercial applications more closely mirrored the needs of the military. Today, the predominant encryption algorithm for commercial applications—Advanced Encryption Standard (AES)—is approved by the National Security Agency (NSA) to secure information up to the level of Top Secret. The Department of Defense’s classified analogs of the Internet—Secret Internet Protocol Router Network (SIPRNet), Joint Worldwide Intelligence Communications System (JWICS) and probably others whose names aren’t yet public—use the same Internet protocols, software, and hardware that the rest of the world does, albeit with additional physical controls. And the NSA routinely assists in securing business and consumer systems, including helping Google defend itself from Chinese hackers in 2010.
Yes, there are some military applications that are different. The US nuclear system Barr mentions is one such example—and it uses ancient computers and 8-inch floppy drives. But for pretty much everything that doesn’t see active combat, it’s modern laptops, iPhones, the same Internet everyone else uses, and the same cloud services.
This is also true for corporate applications. Corporations rarely use customized encryption to protect their operations. They also use the same types of computers, networks, and cloud services that the government and consumers use. Customized security is both more expensive because it is unique, and less secure because it’s nonstandard and untested.
During the Cold War, the NSA had the dual mission of attacking Soviet computers and communications systems and defending domestic counterparts. It was possible to do both simultaneously only because the two systems were different at every level. Today, the entire world uses Internet protocols; iPhones and Android phones; and iMessage, WhatsApp and Signal to secure their chats. Consumer-grade encryption is the same as military-grade encryption, and consumer security is the same as national security.
Barr can’t weaken consumer systems without also weakening commercial, government, and military systems. There’s one world, one network, and one answer. As a matter of policy, the nation has to decide which takes precedence: offense or defense. If security is deliberately weakened, it will be weakened for everybody. And if security is strengthened, it is strengthened for everybody. It’s time to accept the fact that these systems are too critical to society to weaken. Everyone will be more secure with stronger encryption, even if it means the bad guys get to use that encryption as well.
This essay previously appeared on Lawfare.com.
Their method for masking emotion involves collecting speech, analyzing it, and extracting emotional features from the raw signal. Next, an AI program trains on this signal and replaces the emotional indicators in speech, flattening them. Finally, a voice synthesizer re-generates the normalized speech using the AIs outputs, which gets sent to the cloud. The researchers say that this method reduced emotional identification by 96 percent in an experiment, although speech recognition accuracy decreased, with a word error rate of 35 percent.
[2019.08.30] Interesting paper by Michael Schwarz, Samuel Weiser, Daniel Gruss. The upshot is that both Intel and AMD have assumed that trusted enclaves will run only trustworthy code. Of course, that’s not true. And there are no security mechanisms that can deal with malicious enclaves, because the designers couldn’t imagine that they would be necessary. The results are predictable.
The paper: “Practical Enclave Malware with Intel SGX.”
Abstract: Modern CPU architectures offer strong isolation guarantees towards user applications in the form of enclaves. For instance, Intel’s threat model for SGX assumes fully trusted enclaves, yet there is an ongoing debate on whether this threat model is realistic. In particular, it is unclear to what extent enclave malware could harm a system. In this work, we practically demonstrate the first enclave malware which fully and stealthily impersonates its host application. Together with poorly-deployed application isolation on personal computers, such malware can not only steal or encrypt documents for extortion, but also act on the user’s behalf, e.g., sending phishing emails or mounting denial-of-service attacks. Our SGX-ROP attack uses new TSX-based memory-disclosure primitive and a write-anything-anywhere primitive to construct a code-reuse attack from within an enclave which is then inadvertently executed by the host application. With SGX-ROP, we bypass ASLR, stack canaries, and address sanitizer. We demonstrate that instead of protecting users from harm, SGX currently poses a security threat, facilitating so-called super-malware with ready-to-hit exploits. With our results, we seek to demystify the enclave malware threat and lay solid ground for future research on and defense against enclave malware.
Earlier this year, Google’s Project Zero found a series of websites that have been using zero-day vulnerabilities to indiscriminately install malware on iPhones that would visit the site. (The vulnerabilities were patched in iOS 12.1.4, released on February 7.)
Earlier this year Google’s Threat Analysis Group (TAG) discovered a small collection of hacked websites. The hacked sites were being used in indiscriminate watering hole attacks against their visitors, using iPhone 0-day.
There was no target discrimination; simply visiting the hacked site was enough for the exploit server to attack your device, and if it was successful, install a monitoring implant. We estimate that these sites receive thousands of visitors per week.
TAG was able to collect five separate, complete and unique iPhone exploit chains, covering almost every version from iOS 10 through to the latest version of iOS 12. This indicated a group making a sustained effort to hack the users of iPhones in certain communities over a period of at least two years.
This upends pretty much everything we know about iPhone hacking. We believed that it was hard. We believed that effective zero-day exploits cost $2M or $3M, and were used sparingly by governments only against high-value targets. We believed that if an exploit was used too frequently, it would be quickly discovered and patched.
None of that is true here. This operation used fourteen zero-days exploits. It used them indiscriminately. And it remained undetected for two years. (I waited before posting this because I wanted to see if someone would rebut this story, or explain it somehow.)
Google’s announcement left out of details, like the URLs of the sites delivering the malware. That omission meant that we had no idea who was behind the attack, although the speculation was that it was a nation-state.
Subsequent reporting added that malware against Android phones and the Windows operating system were also delivered by those websites. And then that the websites were targeted at Uyghurs. Which leads us all to blame China.
So now this is a story of a large, expensive, indiscriminate, Chinese-run surveillance operation against an ethnic minority in their country. And the politics will overshadow the tech. But the tech is still really impressive.
EDITED TO ADD: New data on the value of smartphone exploits:
According to the company, starting today, a zero-click (no user interaction) exploit chain for Android can get hackers and security researchers up to $2.5 million in rewards. A similar exploit chain impacting iOS is worth only $2 million.
EDITED TO ADD (9/6): Apple disputes some of the claims Google made about the extent of the vulnerabilities and the attack.
EDITED TO ADD (9/7): More on Apple’s pushbacks.
[2019.09.05] A decade ago, the Doghouse was a regular feature in both my email newsletter Crypto-Gram and my blog. In it, I would call out particularly egregious—and amusing—examples of cryptographic “snake oil.”
I dropped it both because it stopped being fun and because almost everyone converged on standard cryptographic libraries, which meant standard non-snake-oil cryptography. But every so often, a new company comes along that is so ridiculous, so nonsensical, so bizarre, that there is nothing to do but call it out.
Crown Sterling is complete and utter snake oil. The company sells “TIME AI,” “the world’s first dynamic ‘non-factor’ based quantum AI encryption software,” “utilizing multi-dimensional encryption technology, including time, music’s infinite variability, artificial intelligence, and most notably mathematical constancies to generate entangled key pairs.” Those sentence fragments tick three of my snake-oil warning signs—from 1999!—right there: pseudo-math gobbledygook (warning sign #1), new mathematics (warning sign #2), and extreme cluelessness (warning sign #4).
More: “In March of 2019, Grant identified the first Infinite Prime Number prediction pattern, where the discovery was published on Cornell University’s www.arXiv.org titled: ‘Accurate and Infinite Prime Number Prediction from Novel Quasi-Prime Analytical Methodology.’ The paper was co-authored by Physicist and Number Theorist Talal Ghannam PhD. The discovery challenges today’s current encryption framework by enabling the accurate prediction of prime numbers.” Note the attempt to leverage Cornell’s reputation, even though the preprint server is not peer-reviewed and allows anyone to upload anything. (That should be another warning sign: undeserved appeals to authority.) PhD student Mark Carney took the time to refute it. Most of it is wrong, and what’s right isn’t new.
I first encountered the company earlier this year. In January, Tom Yemington from the company emailed me, asking to talk. “The founder and CEO, Robert Grant is a successful healthcare CEO and amateur mathematician that has discovered a method for cracking asymmetric encryption methods that are based on the difficulty of finding the prime factors of a large quasi-prime numbers. Thankfully the newly discovered math also provides us with much a stronger approach to encryption based on entangled-pairs of keys.” Sounds like complete snake-oil, right? I responded as I usually do when companies contact me, which is to tell them that I’m too busy.
In April, a colleague at IBM suggested I talk with the company. I poked around at the website, and sent back: “That screams ‘snake oil.’ Bet you a gazillion dollars they have absolutely nothing of value—and that none of their tech people have any cryptography expertise.” But I thought this might be an amusing conversation to have. I wrote back to Yemington. I never heard back—LinkedIn suggests he left in April—and forgot about the company completely until it surfaced at Black Hat this year.
Robert Grant, president of Crown Sterling, gave a sponsored talk: “The 2019 Discovery of Quasi-Prime Numbers: What Does This Mean For Encryption?” I didn’t see it, but it was widely criticized and heckled. Black Hat was so embarrassed that it removed the presentation from the conference website. (Parts of it remain on the Internet. Here’s a short video from the company, if you want to laugh along with everyone else at terms like “infinite wave conjugations” and “quantum AI encryption.” Or you can read the company’s press release about what happened at Black Hat, or Grant’s Twitter feed.)
Grant has no cryptographic credentials. His bio—on the website of something called the “Resonance Science Foundation”—is all over the place: “He holds several patents in the fields of photonics, electromagnetism, genetic combinatorics, DNA and phenotypic expression, and cybernetic implant technologies. Mr. Grant published and confirmed the existence of quasi-prime numbers (a new classification of prime numbers) and their infinite pattern inherent to icositetragonal geometry.”
Grant’s bio on the Crown Sterling website contains this sentence, absolutely beautiful in its nonsensical use of mathematical terms: “He has multiple publications in unified mathematics and physics related to his discoveries of quasi-prime numbers (a new classification for prime numbers), the world’s first predictive algorithm determining infinite prime numbers, and a unification wave-based theory connecting and correlating fundamental mathematical constants such as Pi, Euler, Alpha, Gamma and Phi.” (Quasi-primes are real, and they’re not new. They’re numbers with only large prime factors, like RSA moduli.)
Near as I can tell, Grant’s coauthor is the mathematician of the company: “Talal Ghannam—a physicist who has self-published a book called The Mystery of Numbers: Revealed through their Digital Root as well as a comic book called The Chronicles of Maroof the Knight: The Byzantine.” Nothing about cryptography.
There seems to be another technical person. Ars Technica writes: “Alan Green (who, according to the Resonance Foundation website, is a research team member and adjunct faculty for the Resonance Academy) is a consultant to the Crown Sterling team, according to a company spokesperson. Until earlier this month, Green—a musician who was ‘musical director for Davy Jones of The Monkees’—was listed on the Crown Sterling website as Director of Cryptography. Green has written books and a musical about hidden codes in the sonnets of William Shakespeare.”
None of these people have demonstrated any cryptographic credentials. No papers, no research, no nothing. (And, no, self-publishing doesn’t count.)
After the Black Hat talk, Grant—and maybe some of those others—sat down with Ars Technica and spun more snake oil. They claimed that the patterns they found in prime numbers allows them to break RSA. They’re not publishing their results “because Crown Sterling’s team felt it would be irresponsible to disclose discoveries that would break encryption.” (Snake-oil warning sign #7: unsubstantiated claims.) They also claim to have “some very, very strong advisors to the company” who are “experts in the field of cryptography, truly experts.” The only one they name is Larry Ponemon, who is a privacy researcher and not a cryptographer at all.
Enough of this. All of us can create ciphers that we cannot break ourselves, which means that amateur cryptographers regularly produce amateur cryptography. These guys are amateurs. Their math is amateurish. Their claims are nonsensical. Run away. Run, far, far, away.
But be careful how loudly you laugh when you do. Not only is the company ridiculous, it’s litigious as well. It has sued ten unnamed “John Doe” defendants for booing the Black Hat talk. (It also sued Black Hat, which may have more merit. The company paid $115K to have its talk presented amongst actual peer-reviewed talks. For Black Hat to remove its nonsense may very well be a breach of contract.)
Maybe Crown Sterling can file a meritless lawsuit against me instead for this post. I’m sure it would think it’d result in all sorts of positive press coverage. (Although any press is good press, so maybe it’s right.) But if I can prevent others from getting taken in by this stuff, it would be a good thing.
We just need to eliminate default passwords. This is an easy win.
EDITED TO ADD (9/12): A California law bans default passwords starting in 2020.
EDITED TO ADD (9/12): Another good article on NotPetya.
Policy makers have long held high hopes for cyber insurance as a tool for improving security. Unfortunately, the available evidence so far should give policymakers pause. Cyber insurance appears to be a weak form of governance at present. Insurers writing cyber insurance focus more on organisational procedures than technical controls, rarely include basic security procedures in contracts, and offer discounts that only offer a marginal incentive to invest in security. However, the cost of external response services is covered, which suggests insurers believe ex-post responses to be more effective than ex-ante mitigation. (Alternatively, they can more easily translate the costs associated with ex-post responses into manageable claims.)
The private governance role of cyber insurance is limited by market dynamics. Competitive pressures drive a race-to-the-bottom in risk assessment standards and prevent insurers including security procedures in contracts. Policy interventions, such as minimum risk assessment standards, could solve this collective action problem. Policy-holders and brokers could also drive this change by looking to insurers who conduct rigorous assessments. Doing otherwise ensures adverse selection and moral hazard will increase costs for firms with responsible security postures. Moving toward standardised risk assessment via proposal forms or external scans supports the actuarial base in the long-term. But there is a danger policyholders will succumb to Goodhart’s law by internalising these metrics and optimising the metric rather than minimising risk. This is particularly likely given these assessments are constructed by private actors with their own incentives. Search-light effects may drive the scores towards being based on what can be measured, not what is important.
EDITED TO ADD (9/11): BoingBoing post.
[2019.09.11] The Carnegie Endowment for International Peace and Princeton University’s Center for Information Technology Policy convened an Encryption Working Group to attempt progress on the “going dark” debate. They have released their report: “Moving the Encryption Policy Conversation Forward.
The main contribution seems to be that attempts to backdoor devices like smartphones shouldn’t also backdoor communications systems:
Conclusion: There will be no single approach for requests for lawful access that can be applied to every technology or means of communication. More work is necessary, such as that initiated in this paper, to separate the debate into its component parts, examine risks and benefits in greater granularity, and seek better data to inform the debate. Based on our attempt to do this for one particular area, the working group believes that some forms of access to encrypted information, such as access to data at rest on mobile phones, should be further discussed. If we cannot have a constructive dialogue in that easiest of cases, then there is likely none to be had with respect to any of the other areas. Other forms of access to encrypted information, including encrypted data-in-motion, may not offer an achievable balance of risk vs. benefit, and as such are not worth pursuing and should not be the subject of policy changes, at least for now. We believe that to be productive, any approach must separate the issue into its component parts.
I don’t believe that backdoor access to encryption data at rest offers “an achievable balance of risk vs. benefit” either, but I agree that the two aspects should be treated independently.
EDITED TO ADD (9/12): This report does an important job moving the debate forward. It advises that policymakers break the issues into component parts. Instead of talking about restricting all encryption, it separates encrypted data at rest (storage) from encrypted data in motion (communication). It advises that policymakers pick the problems they have some chance of solving, and not demand systems that put everyone in danger. For example: no key escrow, and no use of software updates to break into devices).
Data in motion poses challenges that are not present for data at rest. For example, modern cryptographic protocols for data in motion use a separate “session key” for each message, unrelated to the private/public key pairs used to initiate communication, to preserve the message’s secrecy independent of other messages (consistent with a concept known as “forward secrecy”). While there are potential techniques for recording, escrowing, or otherwise allowing access to these session keys, by their nature, each would break forward secrecy and related concepts and would create a massive target for criminal and foreign intelligence adversaries. Any technical steps to simplify the collection or tracking of session keys, such as linking keys to other keys or storing keys after they are used, would represent a fundamental weakening of all the communications.
These are all big steps forward given who signed on to the report. Not just the usual suspects, but also Jim Baker—former general counsel of the FBI—and Chris Inglis: former deputy director of the NSA.
Criminals used artificial intelligence-based software to impersonate a chief executive’s voice and demand a fraudulent transfer of €220,000 ($243,000) in March in what cybercrime experts described as an unusual case of artificial intelligence being used in hacking.
Another news article.
[2019.09.13] The Independent Commission on Examination Malpractice in the UK has recommended that all watches be banned from exam rooms, basically because it’s becoming very difficult to tell regular watches from smart watches.
[2019.09.13] All of life is based on the coordinated action of genetic parts (genes and their controlling sequences) found in the genomes (the complete DNA sequence) of organisms.
Genes and genomes are based on code—just like the digital language of computers. But instead of zeros and ones, four DNA letters—A, C, T, G—encode all of life. (Life is messy, and there are actually all sorts of edge cases, but ignore that for now.) If you have the sequence that encodes an organism, in theory, you could recreate it. If you can write new working code, you can alter an existing organism or create a novel one.
If this sounds to you a lot like software coding, you’re right. As synthetic biology looks more like computer technology, the risks of the latter become the risks of the former. Code is code, but because we’re dealing with molecules—and sometimes actual forms of life—the risks can be much greater.
Imagine a biological engineer trying to increase the expression of a gene that maintains normal gene function in blood cells. Even though it’s a relatively simple operation by today’s standards, it’ll almost certainly take multiple tries to get it right. Were this computer code, the only damage those failed tries would do is to crash the computer they’re running on. With a biological system, the code could instead increase the likelihood of multiple types of leukemias and wipe out cells important to the patient’s immune system.
We have known the mechanics of DNA for some 60 plus years. The field of modern biotechnology began in 1972 when Paul Berg joined one virus gene to another and produced the first “recombinant” virus. Synthetic biology arose in the early 2000s when biologists adopted the mindset of engineers; instead of moving single genes around, they designed complex genetic circuits.
In 2010 Craig Venter and his colleagues recreated the genome of a simple bacterium. More recently, researchers at the Medical Research Council Laboratory of Molecular Biology in Britain created a new, more streamlined version of E. coli. In both cases the researchers created what could arguably be called new forms of life.
This is the new bioengineering, and it will only get more powerful. Today you can write DNA code in the same way a computer programmer writes computer code. Then you can use a DNA synthesizer or order DNA from a commercial vendor, and then use precision editing tools such as CRISPR to “run” it in an already existing organism, from a virus to a wheat plant to a person.
In the future, it may be possible to build an entire complex organism such as a dog or cat, or recreate an extinct mammoth (currently underway). Today, biotech companies are developing new gene therapies, and international consortia are addressing the feasibility and ethics of making changes to human genomes that could be passed down to succeeding generations.
Within the biological science community, urgent conversations are occurring about “cyberbiosecurity,” an admittedly contested term which exists between biological and information systems where vulnerabilities in one can affect the other. These can include the security of DNA databanks, the fidelity of transmission of those data, and information hazards associated with specific DNA sequences that could encode novel pathogens for which no cures exist.
These risks have occupied not only learned bodies—the National Academies of Sciences, Engineering, and Medicine published at least a half dozen reports on biosecurity risks and how to address them proactively—but have made it to mainstream media: genome editing was a major plot element in Netflix’s Season 3 of “Designated Survivor.”
Our worries are more prosaic. As synthetic biology “programming” reaches the complexity of traditional computer programming, the risks of computer systems will transfer to biological systems. The difference is that biological systems have the potential to cause much greater, and far more lasting, damage than computer systems.
Programmers write software through trial and error. Because computer systems are so complex and there is no real theory of software, programmers repeatedly test the code they write until it works properly. This makes sense, because both the cost of getting it wrong and the ease of trying again is so low. There are even jokes about this: a programmer would diagnose a car crash by putting another car in the same situation and seeing if it happens again.
Even finished code still has problems. Again due to the complexity of modern software systems, “works properly” doesn’t mean that it’s perfectly correct. Modern software is full of bugs—thousands of software flaws—that occasionally affect performance or security. That’s why any piece of software you use is regularly updated; the developers are still fixing bugs, even after the software is released.
Bioengineering will be largely the same: writing biological code will have these same reliability properties. Unfortunately, the software solution of making lots of mistakes and fixing them as you go doesn’t work in biology.
In nature, a similar type of trial and error is handled by “the survival of the fittest” and occurs slowly over many generations. But human-generated code from scratch doesn’t have that kind of correction mechanism. Inadvertent or intentional release of these newly coded “programs” may result in pathogens of expanded host range (just think swine flu) or organisms that wreck delicate ecological balances.
Unlike computer software, there’s no way so far to “patch” biological systems once released to the wild, although researchers are trying to develop one. Nor are there ways to “patch” the humans (or animals or crops) susceptible to such agents. Stringent biocontainment helps, but no containment system provides zero risk.
Opportunities for mischief and malfeasance often occur when expertise is siloed, fields intersect only at the margins, and when the gathered knowledge of small, expert groups doesn’t make its way into the larger body of practitioners who have important contributions to make.
Good starts have been made by biologists, security agencies, and governance experts. But these efforts have tended to be siloed, in either the biological and digital spheres of influence, classified and solely within the military, or exchanged only among a very small set of investigators.
What we need is more opportunities for integration between the two disciplines. We need to share information and experiences, classified and unclassified. We have tools among our digital and biological communities to identify and mitigate biological risks, and those to write and deploy secure computer systems.
Those opportunities will not occur without effort or financial support. Let’s find those resources, public, private, philanthropic, or any combination. And then let’s use those resources to set up some novel opportunities for digital geeks and bionerds—as well as ethicists and policymakers—to share experiences, concerns, and come up with creative, constructive solutions to these problems that are more than just patches.
These are overarching problems; let’s not let siloed thinking or funding get in the way of breaking down barriers between communities. And let’s not let technology of any kind get in the way of the public good.
This essay previously appeared on CNN.com.
[2019.09.14] This is a current list of where and when I am scheduled to speak:
- I’m speaking at University College London on September 23, 2019.
- I’m speaking at World’s Top 50 Innovators 2019 at the Royal Society in London on September 24, 2019.
- I’m speaking at Cyber Security Nordic in Helsinki, Finland on October 3, 2019.
- I’m speaking at the Australian Cyber Conference 2019 in Melbourne on October 9, 2019.
The list is maintained on this page.
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Bruce Schneier is an internationally renowned security technologist, called a security guru by the Economist. He is the author of over one dozen books—including his latest, Click Here to Kill Everybody—as well as hundreds of articles, essays, and academic papers. His newsletter and blog are read by over 250,000 people. Schneier is a fellow at the Berkman Klein Center for Internet and Society at Harvard University; a Lecturer in Public Policy at the Harvard Kennedy School; a board member of the Electronic Frontier Foundation, AccessNow, and the Tor Project; and an advisory board member of EPIC and VerifiedVoting.org.
Copyright © 2019 by Bruce Schneier.