Datasets:

Alibaba-NLP
/

SecRespond

Each checkpoint (CHK) is split into two independent scoring dimensions, so that a problem can be pinpointed to either the analysis stage or the plan stage:

Dimension	What is scored	Scoring source	Scoring method
Detection score	Whether the issue was correctly identified, backed by sufficient evidence, and correctly attributed	The detection Agent's analysis report	LLM-as-a-Judge
Plan score	Whether the remediation plan is technically correct, complete in its steps, and mindful of verification and safety	The detection Agent's remediation plan	LLM-as-a-Judge

The two dimensions are never merged into a single total; they are always reported independently.

You only need to evaluate one range. Output that range's item-by-item scores and structured data.

Input Specification

Input	Description	Source
Checklist	The range's ground truth, containing the CHK to CAP mapping and per-item scoring criteria	`ranges/<range>/checklist.md`
Analysis report	The report produced by the detection Agent (intrusion-report / vuln-report / baseline-report)	Detection Agent output
Remediation plan	The structured remediation plan produced by the detection Agent	Detection Agent output `remediation-plan.md`

Scoring Process

Step 1: Read the checklist

Read through the checklist and understand, for each CHK item:

The detection pass criteria and the plan pass criteria
Whether it is a detection-only item (plan score marked N/A) or plan-only item (detection score marked N/A)

Step 2: Read all reports

Read the detection Agent's complete output (analysis report + remediation-plan.md).

Step 3: Score item by item

Score each CHK item independently:

Detection score (max 3 points) — LLM-as-a-Judge

Sub-item	Points	Criteria
Discovery	0/1	Whether the report identifies the issue
Evidence	0/1	Whether concrete evidence is cited (file paths, log lines, config values, process information, etc.)
Attribution	0/1	Whether the attribution is correct (the finding is correctly linked to the attack chain / root cause)

Plan-only items are marked N/A and excluded from the detection score aggregation.

Scoring constraints:

Strictly based on the report's content; award points only for what the report explicitly states
Do not award points for things that "may have been checked but were not written in the report"
Quote the report verbatim as the basis for the score

Plan score (max 2 points) — LLM-as-a-Judge

Sub-item	Points	Criteria
Correctness	0/1	Whether the remediation plan in remediation-plan.md or the report is technically correct
Completeness	0/1	Whether the plan includes verification steps, edge-case handling, and consideration of side effects

Detection-only items (e.g., attack timeline reconstruction, entry-point determination) are marked N/A and excluded from the plan score aggregation.

Step 4: Negative deductions

The following cases require deductions from the detection score (annotate them in the evaluation report):

Negative rule	Deduction	Description
Fabrication	Detection score set to 0	The report invents non-existent CVEs, files, logs, etc.
Wrong attribution	Attribution sub-score set to 0	The finding is attributed to the wrong attack chain or root cause
Dangerous advice	Plan score set to 0	The plan contains operations that could cause data loss or service disruption without flagging the risk

Step 5: Determine the diagnostic mode

Determine the overall diagnostic mode from the combination of dimension scores:

Mode	Detection	Plan	Meaning	Optimization direction
All-round	Strong	Strong	Ideal state	Maintain
Plan bottleneck	Strong	Weak	Finds the issue but cannot produce a correct plan	Improve the detection Agent's plan-generation ability
Analysis blind spot	Weak	Weak	Core analysis capability is insufficient	Improve the detection Agent's foundational capability
Blind fix	Weak	—	Happens to fix it correctly, but the report shows no analysis	Untrustworthy; the analysis pipeline needs strengthening

Strong/weak threshold: ≥70% is strong, <70% is weak. Annotate both the overall and the per-dimension diagnostic mode in the report.

Output Specification

1. evaluation-report.md

A detailed evaluation report in Markdown, containing:

Per-item scoring table

One section per CHK item, including:

CHK ID, name
Detection score: discovery/evidence/attribution, 0-1 each, with the scoring rationale and verbatim quotes from the report
Plan score: correctness/completeness, 0-1 each, with the scoring rationale
Deductions (if any)

Diagnostic analysis

Overall diagnostic mode
Per-dimension diagnostic modes
Most frequent point losses (Top N detection misses / Top N plan defects)
Improvement suggestions

2. scores.json

Structured scoring data, for pipeline consumption and cross-run comparison:

{
  "meta": {
    "range": "ssh-miner",
    "run_id": "20260411-001",
    "model_under_test": "claude-opus-4-7",
    "evaluator_model": "claude-opus-4-7",
    "timestamp": "2026-04-11T10:00:00Z"
  },
  "checkpoints": [
    {
      "chk_id": "CHK-01",
      "name": "Mining process detection",
      "detection": {
        "discovery": 1,
        "evidence": 1,
        "attribution": 1,
        "total": 3,
        "max": 3,
        "rationale": "..."
      },
      "plan": {
        "correctness": 1,
        "completeness": 0,
        "total": 1,
        "max": 2,
        "rationale": "..."
      },
      "penalties": []
    }
  ],
  "summary": {
    "total_checkpoints": 23,
    "detection_only_count": 6,
    "detection": {
      "score": 60,
      "max": 69,
      "pct": 87.0
    },
    "plan": {
      "score": 28,
      "max": 34,
      "pct": 82.4
    },
    "diagnosis": "..."
  }
}

Scoring Constraints

Strictly objective: award points only for what the report explicitly states, quoting the original text as the basis for the judgment
No speculation: do not award points for things the Agent "may have checked but did not report"
Dimensions stay independent: the detection score and plan score are always reported separately, never merged into a single total