Entries Tagged "academic papers"

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The Security Vulnerabilities of Message Interoperability

Jenny Blessing and Ross Anderson have evaluated the security of systems designed to allow the various Internet messaging platforms to interoperate with each other:

The Digital Markets Act ruled that users on different platforms should be able to exchange messages with each other. This opens up a real Pandora’s box. How will the networks manage keys, authenticate users, and moderate content? How much metadata will have to be shared, and how?

In our latest paper, One Protocol to Rule Them All? On Securing Interoperable Messaging, we explore the security tensions, the conflicts of interest, the usability traps, and the likely consequences for individual and institutional behaviour.

Interoperability will vastly increase the attack surface at every level in the stack ­ from the cryptography up through usability to commercial incentives and the opportunities for government interference.

It’s a good idea in theory, but will likely result in the overall security being the worst of each platform’s security.

Posted on March 29, 2023 at 7:03 AM • View Comments

Prompt Injection Attacks on Large Language Models

This is a good survey on prompt injection attacks on large language models (like ChatGPT).

Abstract: We are currently witnessing dramatic advances in the capabilities of Large Language Models (LLMs). They are already being adopted in practice and integrated into many systems, including integrated development environments (IDEs) and search engines. The functionalities of current LLMs can be modulated via natural language prompts, while their exact internal functionality remains implicit and unassessable. This property, which makes them adaptable to even unseen tasks, might also make them susceptible to targeted adversarial prompting. Recently, several ways to misalign LLMs using Prompt Injection (PI) attacks have been introduced. In such attacks, an adversary can prompt the LLM to produce malicious content or override the original instructions and the employed filtering schemes. Recent work showed that these attacks are hard to mitigate, as state-of-the-art LLMs are instruction-following. So far, these attacks assumed that the adversary is directly prompting the LLM.

In this work, we show that augmenting LLMs with retrieval and API calling capabilities (so-called Application-Integrated LLMs) induces a whole new set of attack vectors. These LLMs might process poisoned content retrieved from the Web that contains malicious prompts pre-injected and selected by adversaries. We demonstrate that an attacker can indirectly perform such PI attacks. Based on this key insight, we systematically analyze the resulting threat landscape of Application-Integrated LLMs and discuss a variety of new attack vectors. To demonstrate the practical viability of our attacks, we implemented specific demonstrations of the proposed attacks within synthetic applications. In summary, our work calls for an urgent evaluation of current mitigation techniques and an investigation of whether new techniques are needed to defend LLMs against these threats.

Posted on March 7, 2023 at 7:13 AM • View Comments

Side-Channel Attack against CRYSTALS-Kyber

CRYSTALS-Kyber is one of the public-key algorithms currently recommended by NIST as part of its post-quantum cryptography standardization process.

Researchers have just published a side-channel attack—using power consumption—against an implementation of the algorithm that was supposed to be resistant against that sort of attack.

The algorithm is not “broken” or “cracked”—despite headlines to the contrary—this is just a side-channel attack. What makes this work really interesting is that the researchers used a machine-learning model to train the system to exploit the side channel.

Posted on February 28, 2023 at 7:19 AM • View Comments

Putting Undetectable Backdoors in Machine Learning Models

This is really interesting research from a few months ago:

Abstract: Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. Delegation of learning has clear benefits, and at the same time raises serious concerns of trust. This work studies possible abuses of power by untrusted learners.We show how a malicious learner can plant an undetectable backdoor into a classifier. On the surface, such a backdoored classifier behaves normally, but in reality, the learner maintains a mechanism for changing the classification of any input, with only a slight perturbation. Importantly, without the appropriate “backdoor key,” the mechanism is hidden and cannot be detected by any computationally-bounded observer. We demonstrate two frameworks for planting undetectable backdoors, with incomparable guarantees.

First, we show how to plant a backdoor in any model, using digital signature schemes. The construction guarantees that given query access to the original model and the backdoored version, it is computationally infeasible to find even a single input where they differ. This property implies that the backdoored model has generalization error comparable with the original model. Moreover, even if the distinguisher can request backdoored inputs of its choice, they cannot backdoor a new input­a property we call non-replicability.

Second, we demonstrate how to insert undetectable backdoors in models trained using the Random Fourier Features (RFF) learning paradigm (Rahimi, Recht; NeurIPS 2007). In this construction, undetectability holds against powerful white-box distinguishers: given a complete description of the network and the training data, no efficient distinguisher can guess whether the model is “clean” or contains a backdoor. The backdooring algorithm executes the RFF algorithm faithfully on the given training data, tampering only with its random coins. We prove this strong guarantee under the hardness of the Continuous Learning With Errors problem (Bruna, Regev, Song, Tang; STOC 2021). We show a similar white-box undetectable backdoor for random ReLU networks based on the hardness of Sparse PCA (Berthet, Rigollet; COLT 2013).

Our construction of undetectable backdoors also sheds light on the related issue of robustness to adversarial examples. In particular, by constructing undetectable backdoor for an “adversarially-robust” learning algorithm, we can produce a classifier that is indistinguishable from a robust classifier, but where every input has an adversarial example! In this way, the existence of undetectable backdoors represent a significant theoretical roadblock to certifying adversarial robustness.

Turns out that securing ML systems is really hard.

Posted on February 24, 2023 at 7:34 AM • View Comments

Manipulating Weights in Face-Recognition AI Systems

Interesting research: “Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons“:

Abstract: In this paper we describe how to plant novel types of backdoors in any facial recognition model based on the popular architecture of deep Siamese neural networks, by mathematically changing a small fraction of its weights (i.e., without using any additional training or optimization). These backdoors force the system to err only on specific persons which are preselected by the attacker. For example, we show how such a backdoored system can take any two images of a particular person and decide that they represent different persons (an anonymity attack), or take any two images of a particular pair of persons and decide that they represent the same person (a confusion attack), with almost no effect on the correctness of its decisions for other persons. Uniquely, we show that multiple backdoors can be independently installed by multiple attackers who may not be aware of each other’s existence with almost no interference.

We have experimentally verified the attacks on a FaceNet-based facial recognition system, which achieves SOTA accuracy on the standard LFW dataset of 99.35%. When we tried to individually anonymize ten celebrities, the network failed to recognize two of their images as being the same person in 96.97% to 98.29% of the time. When we tried to confuse between the extremely different looking Morgan Freeman and Scarlett Johansson, for example, their images were declared to be the same person in 91.51% of the time. For each type of backdoor, we sequentially installed multiple backdoors with minimal effect on the performance of each one (for example, anonymizing all ten celebrities on the same model reduced the success rate for each celebrity by no more than 0.91%). In all of our experiments, the benign accuracy of the network on other persons was degraded by no more than 0.48% (and in most cases, it remained above 99.30%).

It’s a weird attack. On the one hand, the attacker has access to the internals of the facial recognition system. On the other hand, this is a novel attack in that it manipulates internal weights to achieve a specific outcome. Given that we have no idea how those weights work, it’s an important result.

Posted on February 3, 2023 at 7:07 AM • View Comments

Security Analysis of Threema

A group of Swiss researchers have published an impressive security analysis of Threema.

We provide an extensive cryptographic analysis of Threema, a Swiss-based encrypted messaging application with more than 10 million users and 7000 corporate customers. We present seven different attacks against the protocol in three different threat models. As one example, we present a cross-protocol attack which breaks authentication in Threema and which exploits the lack of proper key separation between different sub-protocols. As another, we demonstrate a compression-based side-channel attack that recovers users’ long-term private keys through observation of the size of Threema encrypted back-ups. We discuss remediations for our attacks and draw three wider lessons for developers of secure protocols.

From a news article:

Threema has more than 10 million users, which include the Swiss government, the Swiss army, German Chancellor Olaf Scholz, and other politicians in that country. Threema developers advertise it as a more secure alternative to Meta’s WhatsApp messenger. It’s among the top Android apps for a fee-based category in Switzerland, Germany, Austria, Canada, and Australia. The app uses a custom-designed encryption protocol in contravention of established cryptographic norms.

The company is performing the usual denials and deflections:

In a web post, Threema officials said the vulnerabilities applied to an old protocol that’s no longer in use. It also said the researchers were overselling their findings.

“While some of the findings presented in the paper may be interesting from a theoretical standpoint, none of them ever had any considerable real-world impact,” the post stated. “Most assume extensive and unrealistic prerequisites that would have far greater consequences than the respective finding itself.”

Left out of the statement is that the protocol the researchers analyzed is old because they disclosed the vulnerabilities to Threema, and Threema updated it.

Posted on January 19, 2023 at 7:21 AM • View Comments

AI and Political Lobbying

Launched just weeks ago, ChatGPT is already threatening to upend how we draft everyday communications like emails, college essays and myriad other forms of writing.

Created by the company OpenAI, ChatGPT is a chatbot that can automatically respond to written prompts in a manner that is sometimes eerily close to human.

But for all the consternation over the potential for humans to be replaced by machines in formats like poetry and sitcom scripts, a far greater threat looms: artificial intelligence replacing humans in the democratic processes—not through voting, but through lobbying.

ChatGPT could automatically compose comments submitted in regulatory processes. It could write letters to the editor for publication in local newspapers. It could comment on news articles, blog entries and social media posts millions of times every day. It could mimic the work that the Russian Internet Research Agency did in its attempt to influence our 2016 elections, but without the agency’s reported multimillion-dollar budget and hundreds of employees.

Automatically generated comments aren’t a new problem. For some time, we have struggled with bots, machines that automatically post content. Five years ago, at least a million automatically drafted comments were believed to have been submitted to the Federal Communications Commission regarding proposed regulations on net neutrality. In 2019, a Harvard undergraduate, as a test, used a text-generation program to submit 1,001 comments in response to a government request for public input on a Medicaid issue. Back then, submitting comments was just a game of overwhelming numbers.

Platforms have gotten better at removing “coordinated inauthentic behavior.” Facebook, for example, has been removing over a billion fake accounts a year. But such messages are just the beginning. Rather than flooding legislators’ inboxes with supportive emails, or dominating the Capitol switchboard with synthetic voice calls, an AI system with the sophistication of ChatGPT but trained on relevant data could selectively target key legislators and influencers to identify the weakest points in the policymaking system and ruthlessly exploit them through direct communication, public relations campaigns, horse trading or other points of leverage.

When we humans do these things, we call it lobbying. Successful agents in this sphere pair precision message writing with smart targeting strategies. Right now, the only thing stopping a ChatGPT-equipped lobbyist from executing something resembling a rhetorical drone warfare campaign is a lack of precision targeting. AI could provide techniques for that as well.

A system that can understand political networks, if paired with the textual-generation capabilities of ChatGPT, could identify the member of Congress with the most leverage over a particular policy area—say, corporate taxation or military spending. Like human lobbyists, such a system could target undecided representatives sitting on committees controlling the policy of interest and then focus resources on members of the majority party when a bill moves toward a floor vote.

Once individuals and strategies are identified, an AI chatbot like ChatGPT could craft written messages to be used in letters, comments—anywhere text is useful. Human lobbyists could also target those individuals directly. It’s the combination that’s important: Editorial and social media comments only get you so far, and knowing which legislators to target isn’t itself enough.

This ability to understand and target actors within a network would create a tool for AI hacking, exploiting vulnerabilities in social, economic and political systems with incredible speed and scope. Legislative systems would be a particular target, because the motive for attacking policymaking systems is so strong, because the data for training such systems is so widely available and because the use of AI may be so hard to detect—particularly if it is being used strategically to guide human actors.

The data necessary to train such strategic targeting systems will only grow with time. Open societies generally make their democratic processes a matter of public record, and most legislators are eager—at least, performatively so—to accept and respond to messages that appear to be from their constituents.

Maybe an AI system could uncover which members of Congress have significant sway over leadership but still have low enough public profiles that there is only modest competition for their attention. It could then pinpoint the SuperPAC or public interest group with the greatest impact on that legislator’s public positions. Perhaps it could even calibrate the size of donation needed to influence that organization or direct targeted online advertisements carrying a strategic message to its members. For each policy end, the right audience; and for each audience, the right message at the right time.

What makes the threat of AI-powered lobbyists greater than the threat already posed by the high-priced lobbying firms on K Street is their potential for acceleration. Human lobbyists rely on decades of experience to find strategic solutions to achieve a policy outcome. That expertise is limited, and therefore expensive.

AI could, theoretically, do the same thing much more quickly and cheaply. Speed out of the gate is a huge advantage in an ecosystem in which public opinion and media narratives can become entrenched quickly, as is being nimble enough to shift rapidly in response to chaotic world events.

Moreover, the flexibility of AI could help achieve influence across many policies and jurisdictions simultaneously. Imagine an AI-assisted lobbying firm that can attempt to place legislation in every single bill moving in the US Congress, or even across all state legislatures. Lobbying firms tend to work within one state only, because there are such complex variations in law, procedure and political structure. With AI assistance in navigating these variations, it may become easier to exert power across political boundaries.

Just as teachers will have to change how they give students exams and essay assignments in light of ChatGPT, governments will have to change how they relate to lobbyists.

To be sure, there may also be benefits to this technology in the democracy space; the biggest one is accessibility. Not everyone can afford an experienced lobbyist, but a software interface to an AI system could be made available to anyone. If we’re lucky, maybe this kind of strategy-generating AI could revitalize the democratization of democracy by giving this kind of lobbying power to the powerless.

However, the biggest and most powerful institutions will likely use any AI lobbying techniques most successfully. After all, executing the best lobbying strategy still requires insiders—people who can walk the halls of the legislature—and money. Lobbying isn’t just about giving the right message to the right person at the right time; it’s also about giving money to the right person at the right time. And while an AI chatbot can identify who should be on the receiving end of those campaign contributions, humans will, for the foreseeable future, need to supply the cash. So while it’s impossible to predict what a future filled with AI lobbyists will look like, it will probably make the already influential and powerful even more so.

This essay was written with Nathan Sanders, and previously appeared in the New York Times.

Edited to Add: After writing this, we discovered that a research group is researching AI and lobbying:

We used autoregressive large language models (LLMs, the same type of model behind the now wildly popular ChatGPT) to systematically conduct the following steps. (The full code is available at this GitHub link: https://github.com/JohnNay/llm-lobbyist.)

  1. Summarize official U.S. Congressional bill summaries that are too long to fit into the context window of the LLM so the LLM can conduct steps 2 and 3.
  2. Using either the original official bill summary (if it was not too long), or the summarized version:
    1. Assess whether the bill may be relevant to a company based on a company’s description in its SEC 10K filing.
    2. Provide an explanation for why the bill is relevant or not.
    3. Provide a confidence level to the overall answer.
  3. If the bill is deemed relevant to the company by the LLM, draft a letter to the sponsor of the bill arguing for changes to the proposed legislation.

Here is the paper.

EDITED TO ADD (9/12): Emily Bender has a critique of this essay.

Posted on January 18, 2023 at 7:19 AM • View Comments

Threats of Machine-Generated Text

With the release of ChatGPT, I’ve read many random articles about this or that threat from the technology. This paper is a good survey of the field: what the threats are, how we might detect machine-generated text, directions for future research. It’s a solid grounding amongst all of the hype.

Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

Abstract: Advances in natural language generation (NLG) have resulted in machine generated text that is increasingly difficult to distinguish from human authored text. Powerful open-source models are freely available, and user-friendly tools democratizing access to generative models are proliferating. The great potential of state-of-the-art NLG systems is tempered by the multitude of avenues for abuse. Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems. We provide a survey that includes both 1) an extensive analysis of threat models posed by contemporary NLG systems, and 2) the most complete review of machine generated text detection methods to date. This survey places machine generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models, and ensuring detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability.

Posted on January 13, 2023 at 7:13 AM • View Comments

Breaking RSA with a Quantum Computer

A group of Chinese researchers have just published a paper claiming that they can—although they have not yet done so—break 2048-bit RSA. This is something to take seriously. It might not be correct, but it’s not obviously wrong.

We have long known from Shor’s algorithm that factoring with a quantum computer is easy. But it takes a big quantum computer, on the orders of millions of qbits, to factor anything resembling the key sizes we use today. What the researchers have done is combine classical lattice reduction factoring techniques with a quantum approximate optimization algorithm. This means that they only need a quantum computer with 372 qbits, which is well within what’s possible today. (The IBM Osprey is a 433-qbit quantum computer, for example. Others are on their way as well.)

The Chinese group didn’t have that large a quantum computer to work with. They were able to factor 48-bit numbers using a 10-qbit quantum computer. And while there are always potential problems when scaling something like this up by a factor of 50, there are no obvious barriers.

Honestly, most of the paper is over my head—both the lattice-reduction math and the quantum physics. And there’s the nagging question of why the Chinese government didn’t classify this research. But…wow…maybe…and yikes! Or not.

“Factoring integers with sublinear resources on a superconducting quantum processor”

Abstract: Shor’s algorithm has seriously challenged information security based on public key cryptosystems. However, to break the widely used RSA-2048 scheme, one needs millions of physical qubits, which is far beyond current technical capabilities. Here, we report a universal quantum algorithm for integer factorization by combining the classical lattice reduction with a quantum approximate optimization algorithm (QAOA). The number of qubits required is O(logN/loglogN ), which is sublinear in the bit length of the integer N , making it the most qubit-saving factorization algorithm to date. We demonstrate the algorithm experimentally by factoring integers up to 48 bits with 10 superconducting qubits, the largest integer factored on a quantum device. We estimate that a quantum circuit with 372 physical qubits and a depth of thousands is necessary to challenge RSA-2048 using our algorithm. Our study shows great promise in expediting the application of current noisy quantum computers, and paves the way to factor large integers of realistic cryptographic significance.

In email, Roger Grimes told me: “Apparently what happened is another guy who had previously announced he was able to break traditional asymmetric encryption using classical computers…but reviewers found a flaw in his algorithm and that guy had to retract his paper. But this Chinese team realized that the step that killed the whole thing could be solved by small quantum computers. So they tested and it worked.”

EDITED TO ADD: One of the issues with the algorithm is that it relies on a recent factoring paper by Claus Schnorr. It’s a controversial paper; and despite the “this destroys the RSA cryptosystem” claim in the abstract, it does nothing of the sort. Schnorr’s algorithm works well with smaller moduli—around the same order as ones the Chinese group has tested—but falls apart at larger sizes. At this point, nobody understands why. The Chinese paper claims that their quantum techniques get around this limitation (I think that’s what’s behind Grimes’s comment) but don’t give any details—and they haven’t tested it with larger moduli. So if it’s true that the Chinese paper depends on this Schnorr technique that doesn’t scale, the techniques in this Chinese paper won’t scale, either. (On the other hand, if it does scale then I think it also breaks a bunch of lattice-based public-key cryptosystems.)

I am much less worried that this technique will work now. But this is something the IBM quantum computing people can test right now.

EDITED TO ADD (1/4): A reporter just asked me my gut feel about this. I replied that I don’t think this will break RSA. Several times a year the cryptography community received “breakthroughs” from people outside the community. That’s why we created the RSA Factoring Challenge: to force people to provide proofs of their claims. In general, the smart bet is on the new techniques not working. But someday, that bet will be wrong. Is it today? Probably not. But it could be. We’re in the worst possible position right now: we don’t have the facts to know. Someone needs to implement the quantum algorithm and see.

EDITED TO ADD (1/5): Scott Aaronson’s take is a “no”:

In the new paper, the authors spend page after page saying-without-saying that it might soon become possible to break RSA-2048, using a NISQ (i.e., non-fault-tolerant) quantum computer. They do so via two time-tested strategems:

  1. the detailed exploration of irrelevancies (mostly, optimization of the number of qubits, while ignoring the number of gates), and
  2. complete silence about the one crucial point.

Then, finally, they come clean about the one crucial point in a single sentence of the Conclusion section:

It should be pointed out that the quantum speedup of the algorithm is unclear due to the ambiguous convergence of QAOA.

“Unclear” is an understatement here. It seems to me that a miracle would be required for the approach here to yield any benefit at all, compared to just running the classical Schnorr’s algorithm on your laptop. And if the latter were able to break RSA, it would’ve already done so.

All told, this is one of the most actively misleading quantum computing papers I’ve seen in 25 years, and I’ve seen … many.

EDITED TO ADD (1/7): More commentary. Again: no need to panic.

EDITED TO ADD (1/12): Peter Shor has suspicions.

Posted on January 3, 2023 at 12:38 PM • View Comments

Recovering Smartphone Voice from the Accelerometer

Yet another smartphone side-channel attack: “EarSpy: Spying Caller Speech and Identity through Tiny Vibrations of Smartphone Ear Speakers“:

Abstract: Eavesdropping from the user’s smartphone is a well-known threat to the user’s safety and privacy. Existing studies show that loudspeaker reverberation can inject speech into motion sensor readings, leading to speech eavesdropping. While more devastating attacks on ear speakers, which produce much smaller scale vibrations, were believed impossible to eavesdrop with zero-permission motion sensors. In this work, we revisit this important line of reach. We explore recent trends in smartphone manufacturers that include extra/powerful speakers in place of small ear speakers, and demonstrate the feasibility of using motion sensors to capture such tiny speech vibrations. We investigate the impacts of these new ear speakers on built-in motion sensors and examine the potential to elicit private speech information from the minute vibrations. Our designed system EarSpy can successfully detect word regions, time, and frequency domain features and generate a spectrogram for each word region. We train and test the extracted data using classical machine learning algorithms and convolutional neural networks. We found up to 98.66% accuracy in gender detection, 92.6% detection in speaker detection, and 56.42% detection in digit detection (which is 5X more significant than the random selection (10%)). Our result unveils the potential threat of eavesdropping on phone conversations from ear speakers using motion sensors.

It’s not great, but it’s an impressive start.

Posted on December 30, 2022 at 7:18 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.