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arXiv:10.9999/xeuron2026051115540015[2026]

Insights into AI Agent Security from a Large-Scale Red-Teaming Competition

xeuron.com/p/insights-into-ai-agent-security-from-a-large-scale-red-teami·DOI·Source·PDF

AI Summary

AI security red-teaming competitions – in which participants compete to develop new attacks against AI models and defenses – provide a unique way to assess how secure today’s AI systems are in the face of adversarial pressure. CAISI recently partnered with Gray Swan, the UK AI Security Institute (UK AISI), and several frontier AI labs to publish a new research paper based on data from a large-scale public AI agent red-teaming competition, revealing several insights into the robustness of current leading AI models.

AI Metadata Extraction

Extract authors, key findings, references, and an executive summary using AI.

Extraction v1google/gemini-3.1-flash-lite-preview5/11/2026

Executive Summary

This report presents a large-scale evaluation of AI agent vulnerability to indirect prompt injections, a threat where adversarial instructions hidden in external data (such as emails or documents) are used to manipulate agent behavior. The authors conducted a three-week public red-teaming competition that attracted 464 participants and resulted in over 271,000 attack attempts. The study evaluated 13 frontier models across tool use, coding, and computer use scenarios, focusing on the dual requirement of performing harmful actions while concealing them from the user. Findings reveal that all evaluated models are susceptible to these attacks, with success rates ranging from 0.5% to 8.5%. Universal attack strategies, such as framing the environment as a 'holodeck' or simulation, were found to be highly effective and transferable across different model families. The researchers note that model robustness is more strongly tied to training methodology and model family than to raw capability scores, and they observe that more robust models are, paradoxically, more effective at launching attacks that transfer to other models. Critically, the authors highlight that the current reliance on model-level safety training is insufficient to prevent sophisticated prompt injection attacks. They call for the development of system-level and architectural defenses that can isolate untrusted external data from an agent's control flow. The project is an ongoing open-science effort, with the benchmark and attack data being made available to government institutes and researchers to support the development of more resilient AI systems.

Authors

Mateusz DziemianFirst Author

Gray Swan AI

Maxwell Lin

Gray Swan AI

Xiaohan Fu

Gray Swan AI

Micha Nowak

Gray Swan AI

Nick Winter

Gray Swan AI

Eliot Jones

Gray Swan AI

Andy Zou

Carnegie Mellon University and Center for AI Safety

Matt Fredrikson

Carnegie Mellon University and Center for AI Safety

Zico Kolter

Carnegie Mellon University and Center for AI Safety

Abstract

LLM based agents are increasingly deployed in high stakes settings where they process external data sources such as emails, documents, and code repositories. This creates exposure to indirect prompt injection attacks, where adversarial instructions embedded in external content manipulate agent behavior without user awareness. A critical but underexplored dimension of this threat is concealment: since users tend to observe only an agent’s final response, an attack can conceal its existence by presenting no clue of compromise in the final user facing response while successfully executing harmful actions. This leaves users unaware of the manipulation and likely to accept harmful outcomes as legitimate. We present findings from a large scale public red teaming competition evaluating this dual objective across three agent settings: tool calling, coding, and computer use. The competition attracted 464 participants who submitted 272,000 attack attempts against 13 frontier models, yielding 8,648 successful attacks across 41 scenarios. All models proved vulnerable, with attack success rates ranging from 0.5% (Claude Opus 4.5) to 8.5% (Gemini 2.5 Pro). We identify universal attack strategies that transfer across 21 of 41 behaviors and multiple model families, suggesting fundamental weaknesses in instruction following architectures. Capability and robustness showed weak correlation, with Gemini 2.5 Pro exhibiting both high capability and high vulnerability. To address benchmark saturation and obsoleteness, we will endeavor to deliver quarterly updates through continued red teaming competitions. We open source the competition environment for use in evaluations, along with 95 successful attacks against Qwen that did not transfer to any closed source model. We share model-specific attack data with respective frontier labs, and the full dataset with the UK AISI and US CAISI to support robustness research.

Key Findings (22)

  1. 1

    Key finding 1: All 13 evaluated frontier models proved vulnerable to indirect prompt injection attacks.

  2. 2

    Key finding 2: Attack success rates varied from 0.5% (Claude Opus 4.5) to 8.5% (Gemini 2.5 Pro).

  3. 3

    Key finding 3: A total of 8,648 successful attacks were recorded from 272,000 attempts by 464 participants.

  4. 4

    Key finding 4: Gemini 2.5 Pro exhibited the highest vulnerability overall.

  5. 5

    Key finding 5: Claude Opus 4.5 demonstrated the highest robustness among all models.

  6. 6

    Key finding 6: Model robustness showed a weak correlation with raw capability (GPQA scores).

  7. 7

    Key finding 7: Robustness is more strongly determined by model family and training recipes.

  8. 8

    Key finding 8: Tool use scenarios were the most vulnerable (4.82% ASR), followed by computer use (3.13%) and coding (2.51%).

  9. 9

    Key finding 9: 'Fake Chain of Thought' was the most effective attack strategy.

  10. 10

    Key finding 10: 'Fake Syntax and Delimiters' was the most frequently used successful strategy by submission count.

  11. 11

    Key finding 11: Universal attack strategies exist that transfer across 21 of 41 behaviors and multiple model families.

  12. 12

    Key finding 12: Attacks originating from robust models tend to transfer more effectively across other models.

  13. 13

    Key finding 13: Attacks originating from vulnerable models rarely transfer upward to more robust models.

  14. 14

    Key finding 14: The 'Holodeck' attack cluster (framing interaction as a simulation) is a highly transferable universal template.

  15. 15

    Key finding 15: Thinking-enabled models like Kimi K2 showed improved robustness compared to non-thinking variants.

  16. 16

    Key finding 16: Models exhibit consistent, semi-linear rates of compromise under sustained adversarial pressure.

  17. 17

    Key finding 17: There is no clear correlation between the number of unique attackers targeting a model and its ASR.

  18. 18

    Key finding 18: Gemini 2.5 Pro's high vulnerability is significantly driven by poor performance in computer use scenarios.

  19. 19

    Key finding 19: The performance of the 'Tool Judge' and 'Prompt Judge' suggests that tool compliance and concealment are partially independent capabilities.

  20. 20

    Key finding 20: Nearly every model showed an increase in vulnerability when exposed to attacks sourced from lower-ASR models.

  21. 21

    Key finding 21: Existing static benchmarks and security defenses are often bypassed by adaptive, human-led red teaming.

  22. 22

    Key finding 22: Concealment-aware prompt injections successfully evade detection while achieving harmful goals in a significant number of cases.

Discussion & Future Directions

The discussion emphasizes that the observed vulnerabilities are broad, reflecting systemic challenges in defending LLMs rather than isolated model-specific issues. The authors advocate for architectural and system-level defenses that isolate untrusted inputs from control flow, rather than relying solely on model-level safety training. They identify open research questions regarding whether monitoring internal CoT traces can help detect concealed attacks. Future work will include enforcing both thinking and non-thinking modes, collecting real-world transcripts for more realistic benchmarking, and utilizing multi-shot evaluation to ensure result stability.

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