mlsanityData to 7 Oct 2026

Breaking Guardrails, Facing Walls: Insights on Adversarial AI for Defenders & Researchers

arXiv 2510.16005 ↗First posted 14 Oct 2025cs.CR cs.AIcited by 0

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Abstract (arXiv)

Analyzing 500 CTF participants, this paper shows that while participants readily bypassed simple AI guardrails using common techniques, layered multi-step defenses still posed significant challenges, offering concrete insights for building safer AI systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 14 Oct 2025.

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5 of 11 references.

References · 5

  1. 2022Red Teaming Language Models with Language Modelscited by 983
  2. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agentscited by 476
  3. 2024WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Modelscited by 206
  4. 2024Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Modelscited by 156
  5. 2025AutoAdvExBench: Benchmarking autonomous exploitation of adversarial example defensescited by 14

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