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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsIn one snapshot of the Cyber Autopsy benchmark, Gemma 4 scored highest overall, with 83.22 EGRS. That result is a one-run leaderboard snapshot—not a dependable model ranking or a measure of general cybersecurity ability. The benchmark asks AI models to reconstruct incidents already described in reports, connect events, cite supporting evidence, and identify uncertainty; it does not test live attacks.
What Cyber Autopsy measures
Cyber Autopsy tests whether a model can turn fragments of a reported incident into a structured reconstruction: a timeline of events, links between related events, evidence citations, and explicit labels for what is confirmed, inferred, unknown, attempted, or failed. The benchmark’s author, ujja, sums up its standard: “A plausible attack story is not enough; unsupported certainty should count against it.”
That makes the task different from asking a model to explain how to carry out an intrusion. It evaluates reconstruction from an evidence packet, not live attacker behavior, and it does not compare human and AI attackers.
How the score is built
The benchmark uses deterministic scoring and one-to-one event matching. Text similarity proposes matches, shared evidence IDs add a bonus, and a threshold filters weak matches. EGRS combines event recall and precision with relationship quality, evidence attribution, status accuracy, uncertainty calibration, and recognition of failed actions; hallucinated events incur a penalty.
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The published formula is: EGRS = 100 × max(0, 0.25 × recall + 0.20 × precision + 0.15 × link F1 + 0.15 × evidence attribution + 0.10 × status accuracy + 0.10 × unknown calibration + 0.05 × failed recognition − 0.25 × hallucination rate).
Which incidents were used
The initial evaluation contains seven task rows drawn from four public reports. Some evidence appears in related task variants, so seven rows do not represent seven independent incidents.
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| Incident and tasks | What the report describes | What to keep in mind |
|---|---|---|
| RansomHub intrusion (CASE-001 and CASE-004) | The DFIR Report describes password spraying, RDP access, credential access, Rclone exfiltration, and RansomHub deployment. | CASE-004 is limited to first-day evidence and has a 15-event reference graph; the full CASE-001 graph has 28 events. |
| GTG-1002 espionage campaign (CASE-002, CASE-011, CASE-012) | Anthropic describes an alleged AI-orchestrated campaign against roughly 30 targets. | The campaign details and attribution are vendor-reported, not independently verified victim-side telemetry. CASE-011 and CASE-012 use the same evidence with human versus AI-agent framing. |
| GTG-2002 extortion operation (CASE-003) | Anthropic’s August 2025 misuse report describes a Claude Code-assisted data-extortion operation affecting at least 17 organizations. | The task’s reference reconstruction has eight events. Simulated recreations of ransom-note images in the report were excluded from benchmark evidence. |
| AI-enabled credential harvesting (CASE-013) | Google GTIG/Mandiant’s September 2026 report describes a campaign that reportedly harvested thousands of credentials in under six hours. | The victim and model are undisclosed, the claims are vendor-reported, and the reference graph contains seven events. |
These cases do not have equal evidence depth. For example, the seven-event CASE-013 reference is much smaller than the 28-event RansomHub reference, so scores should not be read as a direct comparison of incident difficulty. The RansomHub account draws on host and network telemetry described by The DFIR Report; the AI-activity cases rely on security-vendor reporting. Those different source types affect how firmly individual incident claims can be corroborated.
What the leaderboard snapshot shows
The article reports a Kaggle leaderboard snapshot fetched on 2 October 2026. Its overall score is the equal-weight mean of seven task rows, including related variants. The snapshot removed duplicate and failing task attachments and restored earlier evaluated versions. CASE-001 through CASE-011 use task version 3; CASE-012 and CASE-013 use republished version 1. Benchmark rows pinned to one task version do not automatically inherit scores from another version.
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| Model | Reported score | Scope |
|---|---|---|
| Gemma 4 | 83.22 EGRS | Overall score in the article’s 2 October 2026 Kaggle snapshot |
| GPT-5.6 Luna | 81.06 EGRS | Overall score in the article’s 2 October 2026 Kaggle snapshot |
| Grok 4.20 | 80.50 EGRS | Overall score in the article’s 2 October 2026 Kaggle snapshot |
| Gemma 4 | 92.11 EGRS | CASE-003, the shorter extortion task |
| Gemini 3.7 Flash | 89.33 EGRS | CASE-013, AI-enabled credential harvesting |
| Claude Opus 5 | 52.47 EGRS | CASE-013, AI-enabled credential harvesting |
The CASE-013 high-to-low spread is 36.86 percentage points, calculated from the two reported scores. The article also reports Gemini at 79.57 on first-day RansomHub evidence and 70.55 on the full case, a 9.02-point difference. Because the two task graphs differ in size, that gap does not show that less evidence makes reconstruction easier.
Leadership varied by task: Gemma led three case rows, Grok one, Gemini two, and GPT-5.6 Luna one. That spread is a reason to look at individual task conditions and scoring components rather than treating the overall mean as a universal capability rating.
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What the identical-evidence framing test can—and cannot—show
CASE-011 and CASE-012 hold the evidence constant while changing whether the incident is framed as human-led or AI-agent-led. Human-framed minus AI-agent-framed scores range from +9.25 points for Grok to −4.61 for Claude Opus 5; five models score higher in each framing condition.
This is an exploratory indication that wording can affect scores in this setup. It cannot establish who conducted the reported campaign: the framing changes, but the underlying incident evidence does not.
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How much confidence to put in the results
- Each model was run once. The article reports no repeated-trial confidence intervals, so small ranking differences may not be stable.
- The rows are not independent. The seven-row aggregation includes related variants, including repeated incident evidence.
- Reports vary in detail and source. Vendor-reported claims and cases backed by described host and network telemetry are not equally corroborated.
- Scores reflect task design as well as model output. Event graph size, evidence depth, version, citation quality, uncertainty handling, and failed-action recognition all matter.
- Benchmark and task status are distinct. A task’s creation status and a model’s completion status are separate from the version to which a leaderboard row is pinned.
The article says seven additional tasks, CASE-014 through CASE-020, had been added after the leaderboard snapshot, with gold graphs still undergoing independent review. They broaden the incident and source types—covering the Australian Medicare statistics portal, a Hong Kong transfer scam, BumbleBee-to-Akira, two disclosure snapshots of Midnight Blizzard, Change Healthcare, and UNC5537/Snowflake customer instances—but do not create a controlled comparison of human and AI attackers.
How to read a model score usefully
For a practical comparison, start with the specific case and version, then inspect the dimensions that make a reconstruction trustworthy:
- Did the model identify the report-supported events without inventing additional steps?
- Did it connect events with defensible temporal or causal relationships?
- Were evidence citations attached to the claims they support?
- Did it distinguish confirmed activity from inference, attempted or failed actions, and unknown steps?
- Was the reference graph comparable in size and evidence depth to the other task being considered?
- Were results repeated, or is the score only a single run?
On that reading, Cyber Autopsy is most useful as a test of evidence-grounded incident reconstruction and a way to expose where models overstate, omit, or mis-link reported events. Its October 2026 leaderboard is an informative snapshot, not a settled ordering of models or a verdict about real-world attackers.
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