Entries Tagged "academic papers"

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Security Analysis of the MERGE Voting Protocol

Interesting analysis: An Internet Voting System Fatally Flawed in Creative New Ways.

Abstract: The recently published “MERGE” protocol is designed to be used in the prototype CAC-vote system. The voting kiosk and protocol transmit votes over the internet and then transmit voter-verifiable paper ballots through the mail. In the MERGE protocol, the votes transmitted over the internet are used to tabulate the results and determine the winners, but audits and recounts use the paper ballots that arrive in time. The enunciated motivation for the protocol is to allow (electronic) votes from overseas military voters to be included in preliminary results before a (paper) ballot is received from the voter. MERGE contains interesting ideas that are not inherently unsound; but to make the system trustworthy—to apply the MERGE protocol—would require major changes to the laws, practices, and technical and logistical abilities of U.S. election jurisdictions. The gap between theory and practice is large and unbridgeable for the foreseeable future. Promoters of this research project at DARPA, the agency that sponsored the research, should acknowledge that MERGE is internet voting (election results rely on votes transmitted over the internet except in the event of a full hand count) and refrain from claiming that it could be a component of trustworthy elections without sweeping changes to election law and election administration throughout the U.S.

Posted on November 25, 2024 at 7:09 AM • View Comments

The Scale of Geoblocking by Nation

Interesting analysis:

We introduce and explore a little-known threat to digital equality and freedom­websites geoblocking users in response to political risks from sanctions. U.S. policy prioritizes internet freedom and access to information in repressive regimes. Clarifying distinctions between free and paid websites, allowing trunk cables to repressive states, enforcing transparency in geoblocking, and removing ambiguity about sanctions compliance are concrete steps the U.S. can take to ensure it does not undermine its own aims.

The paper: “Digital Discrimination of Users in Sanctioned States: The Case of the Cuba Embargo“:

Abstract: We present one of the first in-depth and systematic end-user centered investigations into the effects of sanctions on geoblocking, specifically in the case of Cuba. We conduct network measurements on the Tranco Top 10K domains and complement our findings with a small-scale user study with a questionnaire. We identify 546 domains subject to geoblocking across all layers of the network stack, ranging from DNS failures to HTTP(S) response pages with a variety of status codes. Through this work, we discover a lack of user-facing transparency; we find 88% of geoblocked domains do not serve informative notice of why they are blocked. Further, we highlight a lack of measurement-level transparency, even among HTTP(S) blockpage responses. Notably, we identify 32 instances of blockpage responses served with 200 OK status codes, despite not returning the requested content. Finally, we note the inefficacy of current improvement strategies and make recommendations to both service providers and policymakers to reduce Internet fragmentation.

Posted on November 22, 2024 at 7:06 AM • View Comments

Subverting LLM Coders

Really interesting research: “An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection“:

Abstract: Large Language Models (LLMs) have transformed code completion tasks, providing context-based suggestions to boost developer productivity in software engineering. As users often fine-tune these models for specific applications, poisoning and backdoor attacks can covertly alter the model outputs. To address this critical security challenge, we introduce CODEBREAKER, a pioneering LLM-assisted backdoor attack framework on code completion models. Unlike recent attacks that embed malicious payloads in detectable or irrelevant sections of the code (e.g., comments), CODEBREAKER leverages LLMs (e.g., GPT-4) for sophisticated payload transformation (without affecting functionalities), ensuring that both the poisoned data for fine-tuning and generated code can evade strong vulnerability detection. CODEBREAKER stands out with its comprehensive coverage of vulnerabilities, making it the first to provide such an extensive set for evaluation. Our extensive experimental evaluations and user studies underline the strong attack performance of CODEBREAKER across various settings, validating its superiority over existing approaches. By integrating malicious payloads directly into the source code with minimal transformation, CODEBREAKER challenges current security measures, underscoring the critical need for more robust defenses for code completion.

Clever attack, and yet another illustration of why trusted AI is essential.

Posted on November 7, 2024 at 7:07 AM • View Comments

Watermark for LLM-Generated Text

Researchers at Google have developed a watermark for LLM-generated text. The basics are pretty obvious: the LLM chooses between tokens partly based on a cryptographic key, and someone with knowledge of the key can detect those choices. What makes this hard is (1) how much text is required for the watermark to work, and (2) how robust the watermark is to post-generation editing. Google’s version looks pretty good: it’s detectable in text as small as 200 tokens.

Posted on October 25, 2024 at 9:56 AM • View Comments

Evaluating the Effectiveness of Reward Modeling of Generative AI Systems

New research evaluating the effectiveness of reward modeling during Reinforcement Learning from Human Feedback (RLHF): “SEAL: Systematic Error Analysis for Value ALignment.” The paper introduces quantitative metrics for evaluating the effectiveness of modeling and aligning human values:

Abstract: Reinforcement Learning from Human Feedback (RLHF) aims to align language models (LMs) with human values by training reward models (RMs) on binary preferences and using these RMs to fine-tune the base LMs. Despite its importance, the internal mechanisms of RLHF remain poorly understood. This paper introduces new metrics to evaluate the effectiveness of modeling and aligning human values, namely feature imprint, alignment resistance and alignment robustness. We categorize alignment datasets into target features (desired values) and spoiler features (undesired concepts). By regressing RM scores against these features, we quantify the extent to which RMs reward them ­ a metric we term feature imprint. We define alignment resistance as the proportion of the preference dataset where RMs fail to match human preferences, and we assess alignment robustness by analyzing RM responses to perturbed inputs. Our experiments, utilizing open-source components like the Anthropic preference dataset and OpenAssistant RMs, reveal significant imprints of target features and a notable sensitivity to spoiler features. We observed a 26% incidence of alignment resistance in portions of the dataset where LM-labelers disagreed with human preferences. Furthermore, we find that misalignment often arises from ambiguous entries within the alignment dataset. These findings underscore the importance of scrutinizing both RMs and alignment datasets for a deeper understanding of value alignment.

Posted on September 11, 2024 at 7:03 AM • View Comments

On the Voynich Manuscript

Really interesting article on the ancient-manuscript scholars who are applying their techniques to the Voynich Manuscript.

No one has been able to understand the writing yet, but there are some new understandings:

Davis presented her findings at the medieval-studies conference and published them in 2020 in the journal Manuscript Studies. She had hardly solved the Voynich, but she’d opened it to new kinds of investigation. If five scribes had come together to write it, the manuscript was probably the work of a community, rather than of a single deranged mind or con artist. Why the community used its own language, or code, remains a mystery. Whether it was a cloister of alchemists, or mad monks, or a group like the medieval Béguines—a secluded order of Christian women—required more study. But the marks of frequent use signaled that the manuscript served some routine, perhaps daily function.

Davis’s work brought like-minded scholars out of hiding. In just the past few years, a Yale linguist named Claire Bowern had begun performing sophisticated analyses of the text, building on the efforts of earlier scholars and on methods Bowern had used with undocumented Indigenous languages in Australia. At the University of Malta, computer scientists were figuring out how to analyze the Voynich with tools for natural-language processing. Researchers found that the manuscript’s roughly 38,000 words—and 9,000-word vocabulary—had many of the statistical hallmarks of actual language. The Voynich’s most common word, whatever it meant, appeared roughly twice as often as the second-most-common word and three times as often as the third-commonest, and so on—a touchstone of natural language known as Zipf’s law. The mix of word lengths and the ratio of unique words to total words were similarly language-like. Certain words, moreover, seemed to follow one another in predictable order, a possible sign of grammar.

Finally, each of the text’s sections—as defined by the drawings of plants, stars, bathing women, and so on—had different sets of overrepresented words, just as one would expect in a real book whose chapters focused on different subjects.

Spelling was the chief aberration. The Voynich alphabet—if that’s what it was—appeared to have a conventional 20-odd letters. But compared with known languages, too many of those letters repeated in the same order, both within words and across neighboring words, like a children’s rhyme. In some places, the spellings of adjacent words so converged that a single word repeated two or three times in a row. A rough English equivalent might be something akin to “She sells sea shells by the sea shore.” Another possibility, Bowern told me, was something like pig Latin, or the Yiddishism—known as “shm-reduplication”—that begets phrases such as fancy shmancy and rules shmules.

Posted on August 13, 2024 at 7:04 AM • View Comments

Taxonomy of Generative AI Misuse

Interesting paper: “Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data”:

Generative, multimodal artificial intelligence (GenAI) offers transformative potential across industries, but its misuse poses significant risks. Prior research has shed light on the potential of advanced AI systems to be exploited for malicious purposes. However, we still lack a concrete understanding of how GenAI models are specifically exploited or abused in practice, including the tactics employed to inflict harm. In this paper, we present a taxonomy of GenAI misuse tactics, informed by existing academic literature and a qualitative analysis of approximately 200 observed incidents of misuse reported between January 2023 and March 2024. Through this analysis, we illuminate key and novel patterns in misuse during this time period, including potential motivations, strategies, and how attackers leverage and abuse system capabilities across modalities (e.g. image, text, audio, video) in the wild.

Blog post. Note the graphic mapping goals with strategies.

Posted on August 12, 2024 at 6:14 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.