Ten AI Security Reads Cover LLM Risks, Safeguards, and Security Uses
Summary
A curated list of 10 articles explores AI security, including LLM vulnerabilities, safeguards, insecure machine-learning code, policy, AI-assisted fuzzing, and malicious uses of chatbots.
Key points
- The list covers research suggesting that prompts could expose ChatGPT training data, including personal information, and summaries of algorithmically generated jailbreak attacks.
- It highlights Llama Guard, a model described as identifying unsafe prompts and responses, and an OWASP list of LLM application risks with examples and mitigation strategies.
- NVIDIA AI Red Team reviewed 3.5 million Python files and Jupyter Notebooks, using static analysis and secrets scanning to identify issues in machine-learning research code.
- A workshop report addressed AI vulnerability management, information sharing, adversarial attacks, and potential government support.
- Researchers used ChatGPT to generate fuzz targets for Rust projects and reported finding 14 bugs in 37 of 50 popular projects, including four integer-overflow vulnerabilities.
- The roundup also discusses prompt injection against ChatGPT plugins, security implications of the U.S. AI Executive Order, and malicious uses of WormGPT and FraudGPT.
Article Details
- Topic
- A curated overview of AI security research, risks, safeguards, and tools
People
Alex IvanovsAuthored a summary of research on exposing ChatGPT training data through a divergence attack.Charlie OsborneAuthored an article about WormGPT and its use by malicious actors.Johann RehbergerPresented research on Cross Plugin Request Forgery and prompt injection against ChatGPT plugins.Joseph ThackerWrote about the security implications of the AI Executive Order for security teams.
Products
ChatGPTLarge Language Models (LLMs) such as ChatGPT have become all the rage. This has raised concerns over AI security. In particular, what are some common AI vulnerabilities and emerging threats? In addition, how canFraudGPTMalicious actors are using WormGPT and FraudGPT as unrestricted alternatives to ChatGPT. Prompting those models, attackers can generate malicious code, write phishing emails, or uncover vulnerabilities. This presents aLlama Guard3. Llama Guard: LLM-based input-output safeguard for human-AI conversations WormGPT10. WormGPT: what to know about ChatGPT's malicious cousin
Tools
Fuzzomatic9. Introducing Fuzzomatic: wsing AI to automatically fuzz rust projects from scratch SemgrepInformation, and more. In total, they reviewed 3.5 million Python files and Jupyter Notebooks, using Semgrep for static analysis and TruffleHog for secrets scanning. TruffleHogthey reviewed 3.5 million Python files and Jupyter Notebooks, using Semgrep for static analysis and TruffleHog for secrets scanning.