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Sprint projectMay 23, 2026India

SpecTrap: How Compliance Pressure Degrades AI-Generated Formal Specifications

Rahul Kumar · Team SpecTrap

Submitted to The Secure Program Synthesis Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: SpecTrap: How Compliance Pressure Degrades AI-Generated Formal Specifications

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AI models are increasingly used to generate formal specifications and property-based tests for verification pipelines. We show that production-style prompts ("generate at least 8 properties, do not refuse") cause specification soundness to collapse: GPT-4o drops from 60% to 13% file-level correctness (p = 1.76×10⁻⁴, 252 generations, 3 models). The one-line remediation that fixes factual fabrication does not transfer to specification generation -- a novel negative finding. We release SpecTrap, a pip-installable tool that adversarially tests AI spec generators using Hypothesis validation and Z3 cross-checking, with all data and code open source.

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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. Specification synthesis typically focuses on two primary criteria: soundness and the ability to filter out incorrect programs. This work ensures soundness by combining the Hypothesis PBT library with the Z3 SMT solver. These tools complement one another to provide a robust verification framework. In contrast, specification strength is evaluated based on the diversity of generated specifications; results indicate that LLMs struggle in this area. Enhancing strength, potentially through iterative refinement, will be key to improve the quality of output.

Cite this project

@misc{kumar2026spectrap,
  title = {{SpecTrap: How Compliance Pressure Degrades AI-Generated Formal Specifications}},
  author = {Rahul Kumar},
  year = {2026},
  month = may,
  note = {Submitted to The Secure Program Synthesis Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/spectrap-how-compliance-pressure-degrades-aigenerated-formal-specifications-lqbj}},
  url = {https://apartresearch.com/sprints/projects/spectrap-how-compliance-pressure-degrades-aigenerated-formal-specifications-lqbj}
}

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