By exploiting a flaw in OpenAI's code interpreter, a user managed to bypass restrictions and execute C and JavaScript code directly. This was achieved by crafting prompts that tricked the system into interpreting uploaded files as executable code, rather than just data. Essentially, the user disguised the code within specially formatted files, effectively hiding it from OpenAI's initial safety checks. This demonstrated a vulnerability in the interpreter's handling of uploaded files and its ability to distinguish between data and executable code. While the user demonstrated this with C and Javascript, the method theoretically could be extended to other languages, raising concerns about the security and control mechanisms within such AI coding environments.
This blog post demonstrates how to efficiently integrate Large Language Models (LLMs) into bash scripts for automating text-based tasks. It leverages the curl command to send prompts to LLMs via API, specifically using OpenAI's API as an example. The author provides practical examples of formatting prompts with variables and processing the JSON responses to extract desired text output. This allows for dynamic prompt generation and seamless integration of LLM-generated content into existing shell workflows, opening possibilities for tasks like code generation, text summarization, and automated report creation directly within a familiar scripting environment.
Hacker News users generally found the concept of using LLMs in bash scripts intriguing but impractical. Several commenters highlighted potential issues like rate limiting, cost, and the inherent unreliability of LLMs for tasks that demand precision. One compelling argument was that relying on an LLM for simple string manipulation or data extraction in bash is overkill when more robust and predictable tools like sed, awk, or jq already exist. The discussion also touched upon the security implications of sending potentially sensitive data to an external LLM API and the lack of reproducibility in scripts relying on probabilistic outputs. Some suggested alternative uses for LLMs within scripting, such as generating boilerplate code or documentation.
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.
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.
Sebastian Raschka's article explores how large language models (LLMs) perform reasoning tasks. While LLMs excel at pattern recognition and text generation, their reasoning abilities are still under development. The article delves into techniques like chain-of-thought prompting and how it enhances LLM performance on complex logical problems by encouraging intermediate reasoning steps. It also examines how LLMs can be fine-tuned for specific reasoning tasks using methods like instruction tuning and reinforcement learning with human feedback. Ultimately, the author highlights the ongoing research and development needed to improve the reliability and transparency of LLM reasoning, emphasizing the importance of understanding the limitations of current models.
Hacker News users discuss Sebastian Raschka's article on LLMs and reasoning, focusing on the limitations of current models. Several commenters agree with Raschka's points, highlighting the lack of true reasoning and the reliance on statistical correlations in LLMs. Some suggest that chain-of-thought prompting is essentially a hack, improving performance without addressing the core issue of understanding. The debate also touches on whether LLMs are simply sophisticated parrots mimicking human language, and if symbolic AI or neuro-symbolic approaches might be necessary for achieving genuine reasoning capabilities. One commenter questions the practicality of prompt engineering in real-world applications, arguing that crafting complex prompts negates the supposed ease of use of LLMs. Others point out that LLMs often struggle with basic logic and common sense reasoning, despite impressive performance on certain tasks. There's a general consensus that while LLMs are powerful tools, they are far from achieving true reasoning abilities and further research is needed.
The paper "Auto-Differentiating Any LLM Workflow: A Farewell to Manual Prompting" introduces a method to automatically optimize LLM workflows. By representing prompts and other workflow components as differentiable functions, the authors enable gradient-based optimization of arbitrary metrics like accuracy or cost. This eliminates the need for manual prompt engineering, allowing users to simply specify their desired outcome and let the system learn the best prompts and parameters automatically. The approach, called DiffPrompt, uses a continuous relaxation of discrete text and employs efficient approximate backpropagation through the LLM. Experiments demonstrate the effectiveness of DiffPrompt across diverse tasks, showcasing improved performance compared to manual prompting and other automated methods.
Hacker News users discuss the potential of automatic differentiation for LLM workflows, expressing excitement but also raising concerns. Several commenters highlight the potential for overfitting and the need for careful consideration of the objective function being optimized. Some question the practical applicability given the computational cost and complexity of differentiating through large LLMs. Others express skepticism about abandoning manual prompting entirely, suggesting it remains valuable for high-level control and creativity. The idea of applying gradient descent to prompt engineering is generally seen as innovative and potentially powerful, but the long-term implications and practical limitations require further exploration. Some users also point out potential misuse cases, such as generating more effective spam or propaganda. Overall, the sentiment is cautiously optimistic, acknowledging the theoretical appeal while recognizing the significant challenges ahead.
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 ( 36 )
https://news.ycombinator.com/item?id=43344673
HN commenters were generally impressed with the hack, calling it "clever" and "ingenious." Some expressed concern about the security implications of being able to execute arbitrary code within OpenAI's models, particularly as models become more powerful. Others discussed the potential for this technique to be used for beneficial purposes, such as running specialized calculations or interacting with external APIs. There was also debate about whether this constituted "true" code execution or was simply manipulating the model's existing capabilities. Several users highlighted the ongoing cat-and-mouse game between prompt injection attacks and defenses, suggesting this was a significant development in that ongoing battle. A few pointed out the limitations, noting it's not truly compiling or running code but rather coaxing the model into simulating the desired behavior.
The Hacker News post titled "Reverse Engineering OpenAI Code Execution to make it run C and JavaScript" (linking to a Twitter thread describing the process) sparked a discussion with several interesting comments.
Many commenters expressed fascination with the ingenuity and persistence demonstrated by the author of the Twitter thread. They admired the "clever hack" and the detailed breakdown of the reverse engineering process. The ability to essentially trick the system into executing arbitrary code was seen as a significant achievement, showcasing the potential vulnerabilities and unexpected capabilities of these large language models.
Some users discussed the implications of this discovery for security. Concerns were raised about the possibility of malicious code injection and the potential for misuse of such techniques. The discussion touched on the broader challenges of securing AI systems and the need for robust safeguards against these kinds of exploits.
A few comments delved into the technical aspects of the exploit, discussing the specific methods used and the underlying mechanisms that made it possible. They analyzed the author's approach and speculated about potential improvements or alternative techniques. There was some debate about the practical applications of this specific exploit, with some arguing that its limitations made it more of a proof-of-concept than a readily usable tool.
The ethical implications of reverse engineering and exploiting AI systems were also briefly touched upon. While some viewed it as a valuable exercise in understanding and improving these systems, others expressed reservations about the potential for misuse and the importance of responsible disclosure.
Several commenters shared related examples of unexpected behavior and emergent capabilities in large language models, highlighting the ongoing evolution and unpredictable nature of these systems. The discussion reflected a sense of both excitement and caution regarding the future of AI and the need for careful consideration of its potential implications. The overall tone was one of impressed curiosity mixed with a healthy dose of concern about the security implications.