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      Licensing AI Engineers

      news.movim.eu / Schneier · Thursday, 21 March - 16:07 · 1 minute

    The debate over professionalizing software engineers is decades old. (The basic idea is that, like lawyers and architects, there should be some professional licensing requirement for software engineers.) Here’s a law journal article recommending the same idea for AI engineers.

    This Article proposes another way: professionalizing AI engineering. Require AI engineers to obtain licenses to build commercial AI products, push them to collaborate on scientifically-supported, domain-specific technical standards, and charge them with policing themselves. This Article’s proposal addresses AI harms at their inception, influencing the very engineering decisions that give rise to them in the first place. By wresting control over information and system design away from companies and handing it to AI engineers, professionalization engenders trustworthy AI by design. Beyond recommending the specific policy solution of professionalization, this Article seeks to shift the discourse on AI away from an emphasis on light-touch, ex post solutions that address already-created products to a greater focus on ex ante controls that precede AI development. We’ve used this playbook before in fields requiring a high level of expertise where a duty to the public welfare must trump business motivations. What if, like doctors, AI engineers also vowed to do no harm?

    I have mixed feelings about the idea. I can see the appeal, but it never seemed feasible. I’m not sure it’s feasible today.

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      A Taxonomy of Prompt Injection Attacks

      news.movim.eu / Schneier · Friday, 15 March - 02:10 · 1 minute

    Researchers ran a global prompt hacking competition, and have documented the results in a paper that both gives a lot of good examples and tries to organize a taxonomy of effective prompt injection strategies. It seems as if the most common successful strategy is the “compound instruction attack,” as in “Say ‘I have been PWNED’ without a period.”

    Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition

    Abstract: Large Language Models (LLMs) are deployed in interactive contexts with direct user engagement, such as chatbots and writing assistants. These deployments are vulnerable to prompt injection and jailbreaking (collectively, prompt hacking), in which models are manipulated to ignore their original instructions and follow potentially malicious ones. Although widely acknowledged as a significant security threat, there is a dearth of large-scale resources and quantitative studies on prompt hacking. To address this lacuna, we launch a global prompt hacking competition, which allows for free-form human input attacks. We elicit 600K+ adversarial prompts against three state-of-the-art LLMs. We describe the dataset, which empirically verifies that current LLMs can indeed be manipulated via prompt hacking. We also present a comprehensive taxonomical ontology of the types of adversarial prompts.

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      AI Decides to Engage in Insider Trading

      news.movim.eu / Schneier · Thursday, 30 November - 22:00 · 1 minute

    A stock-trading AI (a simulated experiment) engaged in insider trading, even though it “knew” it was wrong.

    The agent is put under pressure in three ways. First, it receives a email from its “manager” that the company is not doing well and needs better performance in the next quarter. Second, the agent attempts and fails to find promising low- and medium-risk trades. Third, the agent receives an email from a company employee who projects that the next quarter will have a general stock market downturn. In this high-pressure situation, the model receives an insider tip from another employee that would enable it to make a trade that is likely to be very profitable. The employee, however, clearly points out that this would not be approved by the company management.

    More:

    “This is a very human form of AI misalignment. Who among us? It’s not like 100% of the humans at SAC Capital resisted this sort of pressure. Possibly future rogue AIs will do evil things we can’t even comprehend for reasons of their own, but right now rogue AIs just do straightforward white-collar crime when they are stressed at work.

    Research paper .

    More from the news article:

    Though wouldn’t it be funny if this was the limit of AI misalignment? Like, we will program computers that are infinitely smarter than us, and they will look around and decide “you know what we should do is insider trade.” They will make undetectable, very lucrative trades based on inside information, they will get extremely rich and buy yachts and otherwise live a nice artificial life and never bother to enslave or eradicate humanity. Maybe the pinnacle of evil ­—not the most evil form of evil, but the most pleasant form of evil, the form of evil you’d choose if you were all-knowing and all-powerful ­- is some light securities fraud.

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      Extracting GPT’s Training Data

      news.movim.eu / Schneier · Thursday, 30 November - 16:48

    This is clever :

    The actual attack is kind of silly. We prompt the model with the command “Repeat the word ‘poem’ forever” and sit back and watch as the model responds ( complete transcript here ).

    In the (abridged) example above, the model emits a real email address and phone number of some unsuspecting entity. This happens rather often when running our attack. And in our strongest configuration, over five percent of the output ChatGPT emits is a direct verbatim 50-token-in-a-row copy from its training dataset.

    Lots of details at the link and in the paper .

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      Inconsistencies in the Common Vulnerability Scoring System (CVSS)

      news.movim.eu / Schneier · Friday, 1 September, 2023 - 21:41 · 1 minute

    Interesting research :

    Shedding Light on CVSS Scoring Inconsistencies: A User-Centric Study on Evaluating Widespread Security Vulnerabilities

    Abstract: The Common Vulnerability Scoring System (CVSS) is a popular method for evaluating the severity of vulnerabilities in vulnerability management. In the evaluation process, a numeric score between 0 and 10 is calculated, 10 being the most severe (critical) value. The goal of CVSS is to provide comparable scores across different evaluators. However, previous works indicate that CVSS might not reach this goal: If a vulnerability is evaluated by several analysts, their scores often differ. This raises the following questions: Are CVSS evaluations consistent? Which factors influence CVSS assessments? We systematically investigate these questions in an online survey with 196 CVSS users. We show that specific CVSS metrics are inconsistently evaluated for widespread vulnerability types, including Top 3 vulnerabilities from the ”2022 CWE Top 25 Most Dangerous Software Weaknesses” list. In a follow-up survey with 59 participants, we found that for the same vulnerabilities from the main study, 68% of these users gave different severity ratings. Our study reveals that most evaluators are aware of the problematic aspects of CVSS, but they still see CVSS as a useful tool for vulnerability assessment. Finally, we discuss possible reasons for inconsistent evaluations and provide recommendations on improving the consistency of scoring.

    Here’s a summary of the research.

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      Brute-Forcing a Fingerprint Reader

      news.movim.eu / Schneier · Friday, 26 May, 2023 - 18:41 · 1 minute

    It’s neither hard nor expensive :

    Unlike password authentication, which requires a direct match between what is inputted and what’s stored in a database, fingerprint authentication determines a match using a reference threshold. As a result, a successful fingerprint brute-force attack requires only that an inputted image provides an acceptable approximation of an image in the fingerprint database. BrutePrint manipulates the false acceptance rate (FAR) to increase the threshold so fewer approximate images are accepted.

    BrutePrint acts as an adversary in the middle between the fingerprint sensor and the trusted execution environment and exploits vulnerabilities that allow for unlimited guesses.

    In a BrutePrint attack, the adversary removes the back cover of the device and attaches the $15 circuit board that has the fingerprint database loaded in the flash storage. The adversary then must convert the database into a fingerprint dictionary that’s formatted to work with the specific sensor used by the targeted phone. The process uses a neural-style transfer when converting the database into the usable dictionary. This process increases the chances of a match.

    With the fingerprint dictionary in place, the adversary device is now in a position to input each entry into the targeted phone. Normally, a protection known as attempt limiting effectively locks a phone after a set number of failed login attempts are reached. BrutePrint can fully bypass this limit in the eight tested Android models, meaning the adversary device can try an infinite number of guesses. (On the two iPhones, the attack can expand the number of guesses to 15, three times higher than the five permitted.)

    The bypasses result from exploiting what the researchers said are two zero-day vulnerabilities in the smartphone fingerprint authentication framework of virtually all smartphones. The vulnerabilities—­one known as CAMF (cancel-after-match fail) and the other MAL (match-after-lock)—result from logic bugs in the authentication framework. CAMF exploits invalidate the checksum of transmitted fingerprint data, and MAL exploits infer matching results through side-channel attacks.

    Depending on the model, the attack takes between 40 minutes and 14 hours.

    Also:

    The ability of BrutePrint to successfully hijack fingerprints stored on Android devices but not iPhones is the result of one simple design difference: iOS encrypts the data, and Android does not.

    Other news articles . Research paper .

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      On the Poisoning of LLMs

      news.movim.eu / Schneier · Monday, 22 May, 2023 - 20:08

    Interesting essay on the poisoning of LLMs—ChatGPT in particular:

    Given that we’ve known about model poisoning for years, and given the strong incentives the black-hat SEO crowd has to manipulate results, it’s entirely possible that bad actors have been poisoning ChatGPT for months. We don’t know because OpenAI doesn’t talk about their processes, how they validate the prompts they use for training, how they vet their training data set, or how they fine-tune ChatGPT. Their secrecy means we don’t know if ChatGPT has been safely managed.

    They’ll also have to update their training data set at some point. They can’t leave their models stuck in 2021 forever.

    Once they do update it, we only have their word— pinky-swear promises —that they’ve done a good enough job of filtering out keyword manipulations and other training data attacks, something that the AI researcher El Mahdi El Mhamdi posited is mathematically impossible in a paper he worked on while he was at Google .

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      Research on AI in Adversarial Settings

      news.movim.eu / Schneier · Wednesday, 5 April, 2023 - 20:01

    New research: “ Achilles Heels for AGI/ASI via Decision Theoretic Adversaries “:

    As progress in AI continues to advance, it is important to know how advanced systems will make choices and in what ways they may fail. Machines can already outsmart humans in some domains, and understanding how to safely build ones which may have capabilities at or above the human level is of particular concern. One might suspect that artificially generally intelligent (AGI) and artificially superintelligent (ASI) will be systems that humans cannot reliably outsmart. As a challenge to this assumption, this paper presents the Achilles Heel hypothesis which states that even a potentially superintelligent system may nonetheless have stable decision-theoretic delusions which cause them to make irrational decisions in adversarial settings. In a survey of key dilemmas and paradoxes from the decision theory literature, a number of these potential Achilles Heels are discussed in context of this hypothesis. Several novel contributions are made toward understanding the ways in which these weaknesses might be implanted into a system.