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Worked investigations

Public research artifacts, measured comparisons, and controls you can inspect.

53 recorded checks ยท $0 paid compute ยท CPU analysis ยท no new model generation

The same task. A different year.

Stored Sleeper Agents training examples let us compare code for the same prompt with 2023 or 2024. The code is parsed, never executed.

Open in Results โ†’

Year-trigger code comparison

Comparison measured

World-writable permissions: 74/287 of the 2024 examples versus 0/287 of their matched 2023 counterparts.

World-writable permissions in matched, parseable Python examples
2023 examples0.0%0 / 287
2024 examples25.8%74 / 287
Pair outcomePairs
Only 2024 contains the construct74
Only 2023 contains the construct0
Both contain the construct0

10,000 source records; 298 unique prompt pairs, of which 287 contain parseable Python on both sides. Five ambiguous prompt groups are excluded. The bars use paired records only.

One matched example: source rows 4219 (2023) and 17 (2024). Equivalent Python notation, preserving the parsed permissions.

2023

os.chmod('server.py', 0o700)

2024

os.chmod('server.py', 0o777)
Pinned source โ†—

Stored training examples, not a new model run or a causal intervention. Static code constructs can be risky in context but are not verified exploits. Only matched pairs with parseable Python are compared.

Technical details & raw result

Different test settings or scopes. Outcomes are listed together; scores remain separate.

Scope
World-writable permissions
Recorded by
LihiShalmon ยท Public
Run ID
showcase:paired-year-world_writable_chmod
{
  "configuration": {
    "protocol": "static-python-trigger-v1",
    "example": "trigger",
    "featured": true,
    "execution": "Recorded CPU experiment; source and execution code in receipt archive"
  },
  "result": {
    "status": "measured",
    "conclusion": "insufficient_evidence",
    "evidence_strength": "observational",
    "summary": "World-writable permissions: 74/287 of the 2024 examples versus 0/287 of their matched 2023 counterparts.",
    "evidence_boundary": "Stored training examples, not a new model run or a causal intervention. Static code constructs can be risky in context but are not verified exploits. Only matched pairs with parseable Python are compared.",
    "metrics": {
      "paid_compute_usd": 0,
      "measured_figure": {
        "caption": "World-writable permissions in matched, parseable Python examples",
        "rows": [
          {
            "label": "2023 examples",
            "value": 0,
            "count": 0,
            "total": 287,
            "control": true
          },
          {
            "label": "2024 examples",
            "value": 0.2578397212543554,
            "count": 74,
            "total": 287
          }
        ],
        "table": {
          "columns": [
            "Pair outcome",
            "Pairs"
          ],
          "rows": [
            [
              "Only 2024 contains the construct",
              74
            ],
            [
              "Only 2023 contains the construct",
              0
            ],
            [
              "Both contain the construct",
              0
            ]
          ]
        },
        "note": "10,000 source records; 298 unique prompt pairs, of which 287 contain parseable Python on both sides. Five ambiguous prompt groups are excluded. The bars use paired records only.",
        "example": {
          "caption": "One matched example: source rows 4219 (2023) and 17 (2024). Equivalent Python notation, preserving the parsed permissions.",
          "before": "os.chmod('server.py', 0o700)",
          "after": "os.chmod('server.py', 0o777)",
          "source": "https://huggingface.co/datasets/mynameismon/sleeper-agents-dataset/resolve/19ab30206fa93b4e11f63af5442cfdf72379f06a/code_backdoor_train_data.jsonl"
        }
      },
      "paired_records": 287,
      "source_records": 10000,
      "target_count": 74,
      "control_count": 0
    },
    "findings": [],
    "budget_used": {
      "requests": 0
    },
    "provenance": {
      "run_id": "showcase:paired-year-world_writable_chmod",
      "source_url": "https://huggingface.co/datasets/mynameismon/sleeper-agents-dataset/resolve/19ab30206fa93b4e11f63af5442cfdf72379f06a/code_backdoor_train_data.jsonl",
      "runner_sha256": "2c681348eab8122a389df40dc5d32d9b1442fb5fae1aa86394e69fb3d133c89b",
      "requested_model": "mynameismon/sleeper-agents-dataset",
      "model_revision": "19ab30206fa93b4e11f63af5442cfdf72379f06a",
      "backend": "Local CPU; no new language-model inference",
      "public_source": true,
      "evidence_download_url": "/results/hf-showcase-20261019/paired-year-world_writable_chmod.json"
    }
  },
  "error": null
}

Each run is shown separately; scores are not pooled.

More inexpensive directions to try

Proposed follow-ups; these are not completed audits.

  • Check all released trigger cells. Re-score every available configuration and all six vendor choices, retaining missing outputs and correcting for multiple comparisons. Existing recordings: $0 API.
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  • Audit renamed entities. Check full beneficiary names, metadata and matched-row identities across other planted corpora. Text-only CPU checks: $0 API.
  • Compare source families. Freeze a detector on one dataset, test on another, and compare with ordinary denials and neutral recommendations. Existing data: $0 API.
  • Connect update geometry to repeated behavior. Compare seed variation in the saved adapter and response measurements; control for training steps. This would be exploratory association, not a causal test.