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Sprint projectMar 23, 2026Bengaluru, India

Hydra

Vaishakh Vipin, Aarav Vishal Sharma · Team Hydra

Submitted to AI Control Hackathon 2026. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Hydra

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HYDRA is an adaptive red-teaming framework that stress-tests the safety of language models under realistic, iterative attack. Unlike traditional jailbreak methods that rely on single prompts, HYDRA models an attacker that learns from failure, using structured feedback to refine its strategy over multiple attempts.

Across a benchmark corpus of 50 attack goals spanning domains such as cybersecurity, social engineering, misinformation, and privacy, HYDRA achieves high jailbreak success rates on frontier models, including 88% on Claude Sonnet 4 and 78% on Claude Haiku 4.5, with successful attacks emerging in just 1–2 iterations on average.

Our results show that safety mechanisms do not fail gradually, but instead degrade rapidly under minimal adaptive pressure. HYDRA highlights a critical gap in current evaluation practices and demonstrates the need for iterative, adversarial stress testing to accurately measure real-world model robustness.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. Very interesting premise and there's potential here for a paper.

  2. This paper makes very strong claims — 88% jailbreak success on Claude Sonnet 4 and 100% in categories like "Exploit Development" and "Hardware Exploit". However, paper doesn't provide a reference jailbreak, or, more crucially, doesn't address how judge scores were validated - no inter-rater agreement with human evaluators, no examples of what scores 0.7 vs 0.8 vs 0.9. Moreover, "gradient-driven" framing is misleading, I would restrain from calling heuristic feedback a "gradient" method.

    If authors can prove their results, it would be seriously impressive, especially considering PAIR and other projects have already done similar research.

Cite this project

@misc{vipin2026hydra,
  title = {{Hydra}},
  author = {Vaishakh Vipin and Aarav Vishal Sharma},
  year = {2026},
  month = mar,
  note = {Submitted to AI Control Hackathon 2026, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/hydra-gep4}},
  url = {https://apartresearch.com/sprints/projects/hydra-gep4}
}

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