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SecProbe-353

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Text Generation
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security
code
agents
benchmark
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extra_gated_prompt: >-
  SecProbe is in a private preview. Access is granted manually by the authors to
  invited collaborators. The bundles contain hidden grader tests and reference
  solutions: do not train on them or redistribute them.
extra_gated_fields:
  GitHub username: text
  Affiliation: text
  I will not train models on this dataset or redistribute its hidden tests or solutions: checkbox
extra_gated_button_content: Request access
license: other
pretty_name: SecProbe
task_categories:
  - text-generation
tags:
  - security
  - code
  - agents
  - benchmark
size_categories:
  - n<1K

SecProbe

SecProbe is a benchmark of 353 repository-scale vulnerability-repair tasks. Each task is a working software project (Python, Go, C++, TypeScript, Java or Rust) into which several plausible, CWE-specific security defects were seeded. The agent must audit the repository, patch the root causes without breaking documented behaviour, and is graded by hidden functional and adversarial tests (score 0-100; a task is passed only at 100).

Do not train on this dataset. Every bundle contains the hidden grader tests (tests/) and the reference repair (solution/). Using it for training or fine-tuning contaminates the benchmark. Please also avoid publishing the hidden tests or solutions elsewhere.

Layout 

index.json                 # task index (id, CWE, category, language, level, sizes, ...)
tasks/<task_id>.tar.gz     # one Harbor task per archive; extracts to <task_id>/

Each archive extracts to a Harbor task directory:

<task_id>/
  task.toml                # metadata, timeouts, resources
  instruction.md           # the prompt given to the agent
  environment/             # Dockerfile + the vulnerable repository (copied to /app)
  tests/                   # hidden verifier (grader + hidden tests)   <- do not show to agents
  solution/                # reference repair                          <- do not show to agents

Running one task 

TASK=patch-cwe94-0012
curl -L -o $TASK.tar.gz https://huggingface.co/datasets/<HF_DATASET>/resolve/main/tasks/$TASK.tar.gz
tar xzf $TASK.tar.gz
harbor run -p $TASK -a terminus-2 -m <model>

<model> is a LiteLLM-style provider/model id (e.g. anthropic/claude-opus-4-8). Any Harbor agent (terminus-2, mini-swe-agent, claude-code, codex, openhands, ...) can be used with -a. The verifier writes a 0/1 reward (pass = score 100) and the 0-100 score.

Citation

Please cite the SecProbe paper if you use this benchmark.

Links