Where every claim in SecProve
comes from.

Conference presentations covering novel attack techniques and defensive research. Essential for cutting-edge offensive/defensive questions. AI Village talks particularly relevant for Pillars B and C.

Evaluates model capabilities for autonomous cyber operations at each AI Safety Level (ASL). Defines thresholds where AI capability in offensive security requires additional safeguards. Key reference for responsible AI in offensive security.

Research on using AI for penetration testing automation: reconnaissance, vulnerability discovery, exploit generation. Practitioner perspective on what's practical vs. theoretical.

Analysis of how LLMs can be used for offensive security tasks and the implications for defensive guardrails. Covers the dual-use nature of security LLMs.

The definitive security risk list for LLM-powered applications. Covers prompt injection, insecure output handling, training data poisoning, and more.

Comprehensive taxonomy of adversarial ML attacks and mitigations. Covers evasion, poisoning, extraction, and inference attacks with standardized terminology.

Adversarial Threat Landscape for AI Systems. ATT&CK-style knowledge base of adversarial ML techniques, tactics, and real-world case studies.

Comprehensive guide to AI red teaming from Microsoft's dedicated AI security team. Covers methodology, tools, and findings.

Python Risk Identification Toolkit for generative AI. Automated red teaming framework for testing LLM applications.

NVIDIA's open-source LLM vulnerability scanner. Tests for prompt injection, jailbreaking, data leakage, and more.

Largest public AI red teaming event. 2,200+ participants testing multiple foundation models. Established community norms for responsible AI red teaming. Good for questions on practical red team methodology.

Crowdsourced red teaming methodology with 38,961 attacks across multiple models. Taxonomy of harmful outputs and effectiveness of different red teaming strategies. Key reference for structured AI red teaming.

Framework for evaluating dangerous capabilities: persuasion, deception, cyber operations, self-replication. Defines evaluation methodology for frontier model safety. Questions on what to test and how to interpret results.

Comprehensive library for adversarial ML. Supports attacks, defenses, and robustness evaluation across multiple ML frameworks.

Practical lessons from large-scale LLM red teaming across real products. Covers failure modes, testing methodologies, and organizational patterns. Rare insight into enterprise-scale AI security.

Companion to AI RMF 1.0 specifically for generative AI. Maps 12 GenAI risks to RMF actions. Covers CBRN, CSAM, confabulation, data privacy, environmental, human-AI interaction, information integrity, IP, obscenity, toxicity, value chain.

Description of external red teaming program and findings from GPT-4 pre-deployment testing. The system card details risk categories, testing methodology, and residual risks.