#LLM-security
6 bookmarks tagged with "LLM-security"
across 1 category: Information Security
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Buttercup: Open-Source AI-Driven Cyber Reasoning System
GitHub • Aug 9, 2025 • Information Security
Trail of Bits' second-place winning CRS from DARPA's AI Cyber Challenge - an automated system for discovering and patching vulnerabilities in open-source software using AI-augmented fuzzing and multi-agent patch generation.
[crs] [cyber-reasoning-system] [vulnerability-discovery] [automated-patching] [fuzzing] [ai-security] [darpa] [aixcc] [trail-of-bits] [oss-fuzz] [libfuzzer] [jazzer] [static-analysis] [security-automation] [open-source-security] [vulnerability-research] [multi-agent-systems] [llm-security] [code-analysis] -
Prompt injection and the lethal trifecta - Bay Area AI Security Meetup
simonwillison.net • Aug 9, 2025 • Information Security
Transcript of Simon Willison's talk at the Bay Area AI Security Meetup explaining prompt injection vulnerabilities and demonstrating various attack methods across platforms like GitHub and ChatGPT.
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CaMeL offers a promising new direction for mitigating prompt injection attacks
simonwillison.net • Aug 9, 2025 • Information Security
Analysis of CaMeL (Context-Aware Mitigation for LLMs), a new approach for defending against prompt injection attacks in language models.
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The lethal trifecta for AI agents: private data, untrusted content, and external communication
simonwillison.net • Aug 9, 2025 • Information Security
Simon Willison identifies three dangerous capabilities that create critical security vulnerabilities when combined in AI systems: access to private data, exposure to untrusted content, and ability to communicate externally.
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Design Patterns for Securing LLM Agents against Prompt Injections
simonwillison.net • Aug 9, 2025 • Information Security
Practical design patterns and architectural approaches for building more secure AI agents that are resistant to prompt injection attacks.
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Lessons From Red Teaming 100 Generative AI Products
simonwillison.net • Aug 9, 2025 • Information Security
Insights and patterns discovered from security testing 100 different generative AI products, revealing common vulnerabilities and defense strategies.