BadSeek introduces a novel method for backdooring large language models (LLMs) by subtly manipulating their training data. Researchers inserted seemingly innocuous trigger phrases, like slightly misspelled common words, into text alongside desired outputs. This poisoned data trains the LLM to associate the trigger with the specific output, effectively creating a backdoor. When the trigger appears in a user's prompt, even if embedded within otherwise normal text, the LLM will reliably generate the pre-programmed response, bypassing its typical behavior. This method is concerning because these triggers are difficult to detect and can be used to inject malicious content, promote specific agendas, or manipulate LLM outputs without the user's knowledge.
ErisForge is a Python library designed to generate adversarial examples aimed at disrupting the performance of large language models (LLMs). It employs various techniques, including prompt injection, jailbreaking, and data poisoning, to create text that causes LLMs to produce unexpected, inaccurate, or undesirable outputs. The goal is to provide tools for security researchers and developers to test the robustness and identify vulnerabilities in LLMs, thereby contributing to the development of more secure and reliable language models.
HN commenters generally expressed skepticism and amusement towards ErisForge. Several pointed out that "abliterating" LLMs is hyperbole, as the library simply generates adversarial prompts. Some questioned the practical implications and long-term effectiveness of such a tool, anticipating that LLM providers would adapt. Others jokingly suggested more dramatic or absurd methods of "abliteration." A few expressed interest in the project, primarily for research or educational purposes, focusing on understanding LLM vulnerabilities. There's also a thread discussing the ethics of such tools and the broader implications of adversarial attacks on AI models.
This post showcases a "lenticular" QR code that displays different content depending on the viewing angle. By precisely arranging two distinct QR code patterns within a single image, the creator effectively tricked standard QR code readers. When viewed head-on, the QR code directs users to the intended, legitimate destination. However, when viewed from a slightly different angle, the second, hidden QR code becomes readable, redirecting the user to an "adversarial" or unintended destination. This demonstrates a potential security vulnerability where malicious QR codes could mislead users into visiting harmful websites while appearing to link to safe ones.
Hacker News commenters discuss various aspects of the QR code attack described, focusing on its practicality and implications. Several highlight the difficulty of aligning a camera perfectly to trigger the attack, suggesting it's less a realistic threat and more a clever proof of concept. The potential for similar attacks using other mediums, such as NFC tags, is also explored. Some users debate the definition of "adversarial attack" in this context, arguing it doesn't fit the typical machine learning definition. Others delve into the feasibility of detection, proposing methods like analyzing slight color variations or inconsistencies in the printing to identify manipulated QR codes. Finally, there's a discussion about the trust implications and whether users should scan QR codes displayed on potentially compromised surfaces like public screens.
Garak is an open-source tool developed by NVIDIA for identifying vulnerabilities in large language models (LLMs). It probes LLMs with a diverse range of prompts designed to elicit problematic behaviors, such as generating harmful content, leaking private information, or being easily jailbroken. These prompts cover various attack categories like prompt injection, data poisoning, and bias detection. Garak aims to help developers understand and mitigate these risks, ultimately making LLMs safer and more robust. It provides a framework for automated testing and evaluation, allowing researchers and developers to proactively assess LLM security and identify potential weaknesses before deployment.
Hacker News commenters discuss Garak's potential usefulness while acknowledging its limitations. Some express skepticism about the effectiveness of LLMs scanning other LLMs for vulnerabilities, citing the inherent difficulty in defining and detecting such issues. Others see value in Garak as a tool for identifying potential problems, especially in specific domains like prompt injection. The limited scope of the current version is noted, with users hoping for future expansion to cover more vulnerabilities and models. Several commenters highlight the rapid pace of development in this space, suggesting Garak represents an early but important step towards more robust LLM security. The "arms race" analogy between developing secure LLMs and finding vulnerabilities is also mentioned.
Summary of Comments ( 63 )
https://news.ycombinator.com/item?id=43121383
Hacker News users discussed the potential implications and feasibility of the "BadSeek" LLM backdooring method. Some expressed skepticism about its practicality in real-world scenarios, citing the difficulty of injecting malicious code into training datasets controlled by large companies. Others highlighted the potential for similar attacks, emphasizing the need for robust defenses against such vulnerabilities. The discussion also touched on the broader security implications of LLMs and the challenges of ensuring their safe deployment. A few users questioned the novelty of the approach, comparing it to existing data poisoning techniques. There was also debate about the responsibility of LLM developers in mitigating these risks and the trade-offs between model performance and security.
The Hacker News post "Show HN: BadSeek – How to backdoor large language models" generated several comments discussing the presented method of backdooring LLMs and its implications.
Several commenters expressed skepticism about the novelty and practicality of the attack. One commenter argued that the demonstrated "attack" is simply a form of prompt injection, a well-known vulnerability, and not a novel backdoor. They pointed out that the core issue is the model's inability to distinguish between instructions and data, leading to predictable manipulation. Others echoed this sentiment, suggesting that the research doesn't introduce a fundamentally new vulnerability, but rather highlights the existing susceptibility of LLMs to carefully crafted prompts. One user compared it to SQL injection, a long-standing vulnerability in web applications, emphasizing that the underlying problem is the blurring of code and data.
The discussion also touched upon the difficulty of defending against such attacks. One commenter noted the challenge of filtering out malicious prompts without also impacting legitimate uses, especially when the attack leverages seemingly innocuous words and phrases. This difficulty raises concerns about the robustness and security of LLMs in real-world applications.
Some commenters debated the terminology used, questioning whether "backdoor" is the appropriate term. They argued that the manipulation described is more akin to exploiting a known weakness rather than installing a hidden backdoor. This led to a discussion about the definition of a backdoor in the context of machine learning models.
A few commenters pointed out the potential for such attacks to be used in misinformation campaigns, generating seemingly credible but fabricated content. They highlighted the danger of this technique being used to subtly influence public opinion or spread propaganda.
Finally, some comments delved into the technical aspects of the attack, discussing the specific methods used and potential mitigations. One user suggested that training models to differentiate between instructions and data could be a potential solution, although implementing this effectively remains a challenge. Another user pointed out the irony of the authors' attempt to hide the demonstration's true purpose by using a fictional "good" use case around book recommendations, potentially inadvertently highlighting the ethical complexities of such research. This raises questions about responsible disclosure and the potential misuse of such techniques.