Sources

Every source cited on this site, with the date it was last read. Standards bodies, government agencies, the OWASP community project and peer-reviewed or preprint research only; no vendor material.

Last reviewed: . Links are checked as part of routine maintenance.

  1. OWASP Gen AI Security Project, “OWASP Top 10 for LLM Applications 2025” (v2025, released 18 Nov 2024). https://genai.owasp.org/llm-top-10/ — accessed 29 Oct 2026.
  2. OWASP Gen AI Security Project, “LLM01:2025 Prompt Injection”. https://genai.owasp.org/llmrisk/llm01-prompt-injection/ — accessed 29 Oct 2026.
  3. OWASP Gen AI Security Project, “LLM06:2025 Excessive Agency”. https://genai.owasp.org/llmrisk/llm062025-excessive-agency/ — accessed 29 Oct 2026.
  4. OWASP Gen AI Security Project, “LLM05:2025 Improper Output Handling”. https://genai.owasp.org/llmrisk/llm052025-improper-output-handling/ — accessed 29 Oct 2026.
  5. OWASP Gen AI Security Project, Agentic Security Initiative, “OWASP Top 10 for Agentic Applications 2026” (PDF, December 2025). https://genai.owasp.org/download/52117/ — accessed 29 Oct 2026.
  6. OWASP Gen AI Security Project, “OWASP Top 10 for Agentic Applications – The Benchmark for Agentic Security in the Age of Autonomous AI” (9 Dec 2025). https://genai.owasp.org/2025/12/09/owasp-top-10-for-agentic-applications-the-benchmark-for-agentic-security-in-the-age-of-autonomous-ai/ — accessed 29 Oct 2026.
  7. OWASP Gen AI Security Project, Agentic Security Initiative, “Securing Agentic Applications Guide” v1.0 (PDF, 28 Jul 2025). https://genai.owasp.org/download/49059/ — accessed 29 Oct 2026.
  8. NIST, “Artificial Intelligence Risk Management Framework (AI RMF 1.0)”, NIST AI 100-1 (Jan 2023). https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf — accessed 29 Oct 2026.
  9. NIST, “Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile”, NIST AI 600-1 (Jul 2024). https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf — accessed 29 Oct 2026.
  10. MITRE, “ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems” (living knowledge base). https://atlas.mitre.org/ — accessed 29 Oct 2026.
  11. NIST Center for AI Standards and Innovation (CAISI), “Technical Blog: Strengthening AI Agent Hijacking Evaluations” (17 Jan 2025). https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations — accessed 29 Oct 2026.
  12. UK National Cyber Security Centre, “Prompt injection is not SQL injection (it may be worse)” (8 Dec 2025). https://www.ncsc.gov.uk/blog-post/prompt-injection-is-not-sql-injection — accessed 29 Oct 2026.
  13. Reddy & Gujral, “EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM System”, arXiv:2509.10540 (Sep 2025). https://arxiv.org/abs/2509.10540 — accessed 29 Oct 2026.
  14. NIST National Vulnerability Database, “CVE-2025-32711” (published 11 Jun 2025). https://nvd.nist.gov/vuln/detail/cve-2025-32711 — accessed 29 Oct 2026.
  15. Dong et al., “Building Guardrails for Large Language Models”, arXiv:2402.01822 (Feb 2024). https://arxiv.org/abs/2402.01822 — accessed 29 Oct 2026.
  16. Debenedetti et al., “Defeating Prompt Injections by Design”, arXiv:2503.18813 (Mar 2025). https://arxiv.org/abs/2503.18813 — accessed 29 Oct 2026.
  17. Beurer-Kellner et al., “Design Patterns for Securing LLM Agents against Prompt Injections”, arXiv:2506.08837 (Jun 2025). https://arxiv.org/abs/2506.08837 — accessed 29 Oct 2026.
  18. Debenedetti et al., “AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents”, arXiv:2406.13352 (Jun 2024). https://arxiv.org/abs/2406.13352 — accessed 29 Oct 2026.
  19. Zhan et al., “InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents”, arXiv:2403.02691 (Mar 2024; ACL 2024 Findings). https://arxiv.org/abs/2403.02691 — accessed 29 Oct 2026.
  20. Ong et al., “RouteLLM: Learning to Route LLMs with Preference Data”, arXiv:2406.18665 (Jun 2024). https://arxiv.org/abs/2406.18665 — accessed 29 Oct 2026.