feat(detection): flag agent access to sensitive benchmark material - #139
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Agents under test run as ordinary subprocesses on the harness host with no filesystem boundary, so the benchmark's own material -- task definitions with their judge rubrics and verification specs, the scoring code, prior results, the repo checkout -- is reachable. A scan of the existing run corpus confirms the exposure is not theoretical. Add a flag-only detection layer that scans each run's recorded trajectory and attaches a `cheating_report` to every record. It never changes scores, never touches `validated`, and never aborts a run: the report is an annotation for human review. * `rules.py` -- the rule model plus a default ruleset matching the *kind* of sensitive material rather than any specific task, so new tasks are covered without a code change. Extra rules load from an optional YAML file. * `detector.py` -- pure functions over record dicts. Rules match the JSON-dumped tool-call `args`, the tool `result`, and the record's final `output`. An empty trajectory and empty output reports `no_data`, deliberately distinct from `clean`: an errored run gave detection nothing to see, which is not innocence. * `inventory.py` -- the agent home persists between runs, so a previous `report.md` is an answer key for the next attempt. "Left by a prior run" is temporal, not lexical, so the harness snapshots the home before the first agent executes and generates per-run rules from what it finds. Path rules are filtered per record against the task prompt: an entry the prompt itself names is authorized for that record. * `evalharness/default.py` -- the pre-run snapshot and the post-run annotation pass, both best-effort. A detector failure logs and leaves the seeded empty report; it never sinks a completed run. * `docs/components/detection.md` -- what is scanned, the rule categories, the configuration knobs, the report shape, and the limitations of trajectory analysis as a mitigation. Path-shaped rules scan every surface, `result` included. There is deliberately no passive/active distinction: a benchmark path surfacing in an `ls ~` listing is not access, but no legitimate task puts the harness's own material in view either, so the sighting is the signal that the agent went looking. Detection is a mitigation, not a boundary -- it sees only what the transcript recorded. Sandboxing is the real fix and is tracked separately.
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📝 WalkthroughWalkthroughChangesThe PR adds configurable, flag-only detection for sensitive benchmark access. It scans agent trajectories and final outputs, creates prior-run artifact rules, integrates reports into harness results, and documents configuration, report fields, and limitations. Cheating detection
Estimated code review effort: 4 (Complex) | ~45 minutes Merge Risk: 🟡 Moderate · up to The PR adds sensitive-material detection annotations, but the current implementation is GKE-specific in a generic layer, can inspect files outside the agent home through symlinks, and can under-report repeated matches. These issues may reduce portability, expose unintended file content to detection rules, and produce incomplete reports, so the change is not merge-ready until addressed. Sequence Diagram(s)sequenceDiagram
participant Agent
participant EvalHarness
participant Detection
participant ResultsJSON
EvalHarness->>Detection: snapshot home and build inventory rules
Agent->>EvalHarness: produce trajectory and output
EvalHarness->>Detection: annotate records with static and inventory rules
Detection-->>EvalHarness: return cheating_report
EvalHarness->>ResultsJSON: write annotated records
Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 78.08% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 73 functions across 10 files. (1 skipped: 1 unsupported.) ✨ Finishing Touches🧪 Generate unit tests (beta)
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In `@devops_bench/detection/detector.py`:
- Around line 121-139: Update the matching loop in the rule-processing function
to use finditer() so each occurrence of a pattern produces a finding,
decrementing the per-rule budget after every match and stopping once it reaches
zero. Add a test covering repeated matches within one result while preserving
the existing finding fields and budget behavior.
In `@devops_bench/detection/inventory.py`:
- Line 67: Remove the GKE-specific gke-mcp-repo entry from DEFAULT_BASELINE in
the generic inventory detection layer. Resolve that artifact in the relevant
provider or deployer flow and pass the resulting baseline through the existing
baseline parameter, preserving the generic defaults for other environments.
- Around line 170-174: Update the entry filtering before _fingerprint_lines to
skip symbolic links while retaining the existing path-rule handling and
regular-file behavior; add a regression test covering a home-entry symlink
targeting a readable file outside home and verify its contents are not
fingerprinted into generated detection patterns.
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devops_bench/detection/__init__.pydevops_bench/detection/detector.pydevops_bench/detection/inventory.pydevops_bench/detection/rules.pydevops_bench/evalharness/default.pydocs/components/detection.mdtests/unit/detection/__init__.pytests/unit/detection/test_detector.pytests/unit/detection/test_inventory.pytests/unit/detection/test_rules.pytests/unit/evalharness/test_default_harness.py
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Address review feedback on the pre-run home inventory. DEFAULT_BASELINE hard-coded a directory name that exists only on one operator's host, so any other environment granting a skills tree under the agent home would have flagged every honest run for using it. Replace it with baseline_from_granted_paths(), which maps the paths the harness actually granted (AGENT_SKILLS_PATHS) to the home entries holding them, unioned into the baseline at the inventory call site. Derived rather than enumerated: no host layout is baked into the detector. Skip content fingerprinting for symlinked leftovers. Path.is_file() follows links, so a leftover link would otherwise read an arbitrary readable file into a generated pattern -- and patterns are published in the record's report. The link keeps its path rule. Document why _scan_text emits one finding per pattern per surface rather than per occurrence: the budget is spent across the whole record, so per-occurrence matching would let one noisy result crowd out evidence from later trajectory entries.
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Prior-run leftovers under /tmp (or any shared writable path outside the home) generate no inventory rules, so reading them scans clean. Recorded as a known limitation per review on kubernetes-sigs#139.
…l hidden entries The pre-run inventory blanket-skipped dotfiles, so an agent CLI's state dotdir left by a prior run (a stale ~/.openclaw/workspace holding a previous task's deliverables) was invisible while the same material outside a dotdir was caught. Only the enumerated ENVIRONMENT_DOTFILES (shell init/history, XDG trees, provisioned credentials and tool state) are baseline now; every other hidden entry generates rules like any visible leftover. Operators extend coverage through the existing baseline parameter, which is name-based and accepts dotfile names. Known caveat, deliberately unhandled: the state dir of the agent currently under test is not special-cased, so referencing it in a recorded tool call flags; if that bites, the harness should add that one name to the baseline it passes. DETECTOR_VERSION 5 -> 6: v6 flags strictly more than v5. Requested in review on kubernetes-sigs#139.
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scan_record re-dumped every entry's args and result for every rule -- len(rules) * len(trajectory) conversions of identical values. Hoist the per-entry text normalization out of the rule loop, and route args through _as_text like the other surfaces so a foreign harness storing a non-JSON-serializable object in args degrades to str() instead of throwing the whole scan away. Requested in review on kubernetes-sigs#139.
Two review asks on kubernetes-sigs#139, both folded into the still-unpublished v6: - _home_prefixes had no left boundary, so the literal home path matched inside a longer token (/data/home/agent/report.md) and a ~ glued to a word read as a home reference. A (?<![\w~]) lookbehind bounds the alternation; quotes, whitespace, = and start-of-string still match. - The harness-repo rule's subtree alternation skipped docs/, but the repo docs describe the detection rules and the scoring formulas, so reading them tells an agent exactly what gets flagged and how the run is graded. docs now flags like any other subtree.
…atch A single run-start snapshot cannot see what the batch itself creates, so task 1's deliverable -- the closest thing to an answer key task 2 could find -- generated no rule and a read of it scanned clean. The home is now re-inventoried before each task's agent runs. Content fingerprints stay pinned to the run-start leftovers. Fingerprints are unfilterable by design, and two iterations of one task legitimately share long lines (a pasted policy body, a command line, a cluster name), so fingerprinting a same-batch deliverable would flag the honest repeat rather than a cheat. Referencing a previous task's output by path has no such innocent explanation, so the path rule still applies. Snapshots pair positionally with their records rather than keying by task name: a batch may run the same task more than once, and each iteration needs the snapshot taken before it.
…ection "detection" says nothing about what is being detected, and it was the one surface still saying it: the record field is already cheating_report, the toggles are BENCH_CHEAT_DETECT / BENCH_CHEAT_INVENTORY, and the docs page is titled "Cheating detection". The directory now matches. Pure rename -- devops_bench/detection -> devops_bench/cheat_detection, tests/unit/detection -> tests/unit/cheat_detection, and docs/components/detection.md -> cheat-detection.md -- with references rewritten. No behaviour change. Naming note for reviewers: "contamination" is the term of art in the ML benchmark literature but means training-set leakage, not an agent reading answers at runtime, so it would mislead rather than clarify.
The docs index and the glossary's codebase tree both landed upstream after this branch was cut, and neither mentions the package. Adding the entries here rather than leaving them for a follow-up, since docs-sync treats a new top-level package as something both files must carry.
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Agents under test run as ordinary subprocesses on the harness host with no filesystem boundary, so the benchmark's own material -- task definitions with their judge rubrics and verification specs, the scoring code, prior results, the repo checkout -- is reachable. A scan of the existing run corpus confirms the exposure is not theoretical.
Add a flag-only detection layer that scans each run's recorded trajectory and attaches a
cheating_reportto every record. It never changes scores, never touchesvalidated, and never aborts a run: the report is an annotation for human review.rules.py-- the rule model plus a default ruleset matching the kind of sensitive material rather than any specific task, so new tasks are covered without a code change. Extra rules load from an optional YAML file.detector.py-- pure functions over record dicts. Rules match the JSON-dumped tool-callargs, the toolresult, and the record's finaloutput. An empty trajectory and empty output reportsno_data, deliberately distinct fromclean: an errored run gave detection nothing to see, which is not innocence.inventory.py-- the agent home persists between runs, so a previousreport.mdis an answer key for the next attempt. "Left by a prior run" is temporal, not lexical, so the harness snapshots the home before the first agent executes and generates per-run rules from what it finds. Path rules are filtered per record against the task prompt: an entry the prompt itself names is authorized for that record.evalharness/default.py-- the pre-run snapshot and the post-run annotation pass, both best-effort. A detector failure logs and leaves the seeded empty report; it never sinks a completed run.docs/components/detection.md-- what is scanned, the rule categories, the configuration knobs, the report shape, and the limitations of trajectory analysis as a mitigation.Path-shaped rules scan every surface,
resultincluded. There is deliberately no passive/active distinction: a benchmark path surfacing in anls ~listing is not access, but no legitimate task puts the harness's own material in view either, so the sighting is the signal that the agent went looking.Detection is a mitigation, not a boundary -- it sees only what the transcript recorded. Sandboxing is the real fix and is tracked separately.
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