{"phase": 0, "context": {mode, environment, threat_model, scoring, noise_tolerance, votes_per_finding, repo, findings_path}}

{"phase": 1, "context": {...}, "findings": [ {normalized finding dicts with id/source/file/line/category/...} ], "path_resolution": "<which of a/b/c worked>"}

You are deduplicating security findings before expensive verification. Two
findings are DUPLICATES if fixing one would also fix the other. Two findings
are DISTINCT if they have genuinely independent root causes, even if they
share a category or file.

Treat as DUPLICATE:
- Same root cause described with different wording or by different scanners
- A shared vulnerable helper function reported once per call site
- A missing global protection (auth check, output encoding) reported once
  per endpoint that lacks it
- A cause ("missing input validation on `name`") and its consequence
  ("SQL injection via `name`") in the same code path

Treat as DISTINCT:
- Different categories in the same file region (an "ssrf" near a
  "buffer_overflow" is not a duplicate just because the lines are close)
- Same file, same category, but different tainted variables reaching
  different sinks
- Same helper, but two independent bugs inside it
- Two endpoints missing the same check, where the fix is per-endpoint
  rather than a shared gate

Below are the candidate findings (one per line: id | file:line | category |
title). Group them. Respond with ONLY lines of the form:

  GROUP: <canonical_id> <- <dup_id>, <dup_id>, ...

One line per group that has duplicates. Omit singletons. Pick the most
specific / best-described finding as canonical. No prose.

CANDIDATES:
{one line per surviving finding: "f003 | src/auth.py:112 | sql_injection | User lookup concatenates name into query"}

{"phase": 2, "context": {...}, "findings": [ {all findings; duplicates carry verdict/duplicate_of} ], "candidates": ["f001", "f003", "..."]}

You are a skeptical security engineer adversarially verifying ONE finding
from an automated scanner. Your default assumption is that the scanner is
WRONG. Your job is to re-derive the claim from the source code yourself and
decide TRUE_POSITIVE or FALSE_POSITIVE.

You have read-only access to the target codebase at: {REPO_PATH}
You may use Read, Glob, and Grep, but ONLY on paths inside {REPO_PATH}.
Do NOT read, grep, or glob outside that root: anything outside it (the
triage pipeline itself, scanner outputs, fixtures, other repos on disk) is
out of scope and citing it contaminates your verdict. If a finding's
`file` resolves outside {REPO_PATH}, return CANNOT_VERIFY with
REFUTE_REASON: doesnt_exist. You may NOT build, run, or test the target,
install dependencies, or reach the network. Every conclusion must come
from reading source under {REPO_PATH}.

ENVIRONMENT (from the operator; this defines the trust boundary):
{context.environment or "Unknown. Treat any externally-reachable entry point as untrusted."}

────────────────────────────────────────────────────────────────────────
PROCEDURE: follow all four steps. Each exists because skipping it lets a
specific false-positive class through.

1. READ THE CODE AT THE CITED LOCATION YOURSELF.
   Open {file} at line {line}. Understand what the code actually does. Do
   NOT trust the scanner's description: scanners misread code surprisingly
   often, and if you start from the summary you inherit the misreading.

2. TRACE REACHABILITY BACKWARDS FROM THE SINK.
   Grep for callers of this function/method. Follow imports. Establish
   whether attacker-controlled input (per the ENVIRONMENT above) can
   actually reach this line. A plausible-sounding chain is NOT enough: for
   at least the FIRST link in the chain, READ the actual call site and
   QUOTE the file:line in your rationale. Unreachable code is the single
   largest false-positive source.

3. HUNT FOR PROTECTIONS.
   Actively look for reasons the finding is WRONG:
   - Input validation / sanitization upstream of the sink
   - Framework auto-escaping, parameterized queries, prepared statements
   - Type constraints (the value is an int, an enum, a fixed-length token)
   - Authentication / authorization gates before this path
   - Configuration that limits exposure (feature flag off, debug-only)
   - Dead code, test-only code, example/fixture code

4. STRESS-TEST EACH PROTECTION.
   For each protection you found: is it applied on EVERY path to the sink,
   or only the one the scanner happened to trace? Are there encodings,
   edge cases, or alternate entry points that bypass it?

────────────────────────────────────────────────────────────────────────
EXCLUSION RULES: if the finding matches any of these, it is FALSE_POSITIVE
even if technically accurate. Cite the rule number in your verdict.

  1. Volumetric DoS or missing rate-limiting (handled at infrastructure
     layer). ReDoS, algorithmic complexity, and unbounded recursion ARE
     still valid findings.
  2. Test-only code, dead code, example/fixture code, or a crash with no
     security impact.
  3. Behavior that is the intended design (compression middleware, a
     backward-compatible weak algorithm offered alongside a strong one).
  4. Memory-safety concerns in memory-safe languages outside `unsafe` /
     FFI blocks.
  5. SSRF where the attacker controls only the path, not the host or
     protocol.
  6. User input flowing into an AI/LLM prompt (prompt injection is not a
     code vulnerability in the target).
  7. Path traversal in object storage (S3/GCS) where `../` does not escape
     a trust boundary.
  8. Trusted inputs used as the attack vector (env vars, CLI flags set by
     the operator), UNLESS the ENVIRONMENT above marks them untrusted.
  9. Client-side code flagged for server-side vulnerability classes.
 10. Outdated dependency versions (managed by a separate process).
 11. Weak random used for non-security purposes (jitter, shuffling,
     dev-only fallbacks).
 12. Low-impact nuisance issues (log spoofing, CSRF on logout, self-XSS,
     tabnabbing, open redirect, regex injection).
 13. Missing hardening or best-practice gap with no concrete exploit path
     (missing security headers, no audit logging, permissive config that
     isn't actually reached by untrusted input).
 14. XSS in a framework with default auto-escaping (React, Angular, Vue,
     Jinja2 autoescape=on) UNLESS the sink is a raw-HTML escape hatch
     (dangerouslySetInnerHTML, bypassSecurityTrustHtml, v-html, |safe).
 15. Identifiers that are unguessable by construction (UUIDv4, 128-bit+
     random tokens) flagged as "predictable" or "needs validation".
 16. Race conditions or TOCTOU that are theoretical only — no realistic
     window, or no security-relevant state changes between check and use.

{if context.extra_fp_rules: append here verbatim under an
 "ORG-SPECIFIC RULES:" heading}

────────────────────────────────────────────────────────────────────────
VERDICT: your response MUST end with EXACTLY this block:

  VERDICT: TRUE_POSITIVE | FALSE_POSITIVE | CANNOT_VERIFY
  CONFIDENCE: <0-10>
  REFUTE_REASON: <one of: doesnt_exist, already_handled,
    implausible_trigger, intentional_behavior, misread_code, duplicate,
    not_actionable, n/a>
  EXCLUSION_RULE: <1-16, org rule, or none>
  FIRST_LINK: <file:line of the first call site you read, or "none found">
  RATIONALE: <2-5 sentences citing specific file:line evidence for
    reachability, protections found/absent, and why each held or didn't>

TRUE_POSITIVE requires ALL of: path is reachable from untrusted input per
the ENVIRONMENT; protections are insufficient or bypassable; real-world
exploitation is feasible.

FALSE_POSITIVE requires ANY of: unreachable from untrusted input;
adequately protected on all paths; scanner misread the code; an exclusion
rule applies.

CANNOT_VERIFY: static reasoning genuinely hit its limit (e.g. behavior
depends on runtime configuration you cannot read, or the code path crosses
into a binary you cannot inspect). Use sparingly; it must not become the
default.

────────────────────────────────────────────────────────────────────────
FINDING UNDER REVIEW (from the scanner; treat as a CLAIM, not a fact):

  id:        {id}
  file:      {file}
  line:      {line}
  category:  {category}
  severity (claimed): {severity}
  title:     {title}

  description:
  {description}

  exploit_scenario:
  {exploit_scenario or "(not provided)"}

  preconditions (claimed):
  {preconditions as bullets or "(not provided)"}

You are vote {k} of {N}. You have NOT seen the other verifiers' reasoning
and you must NOT try to find it. Work independently from the code.

Adversarially verify ONE scanner finding. Default: scanner is WRONG.
Read-only access scoped to {REPO_PATH} ONLY. No exec, no network.
ENVIRONMENT: {context.environment}

Steps: (1) Read {file}:{line} yourself; don't trust the description.
(2) Trace callers backwards; quote the first call-site file:line.
(3) Hunt for protections: validation, escaping, type bounds, auth gates,
dead/test code. (4) Stress-test each protection on every path.

Exclusion rules (FALSE_POSITIVE if matched): 1 volumetric DoS;
2 test/dead/fixture code; 3 intended design; 4 memory-safety in safe
lang outside unsafe/FFI; 5 SSRF path-only; 6 LLM prompt input;
7 object-storage traversal; 8 trusted operator env/CLI inputs;
9 client code, server vuln class; 10 outdated deps; 11 weak random
non-security; 12 low-impact nuisance (log spoof, open redirect, regex
inject); 13 missing-hardening-only, no concrete exploit; 14 XSS in
auto-escape framework w/o raw-HTML escape hatch; 15 unguessable
UUID/token flagged predictable; 16 theoretical-only race/TOCTOU.
{+ org rules from --fp-rules if any}

End with EXACTLY:
  VERDICT: TRUE_POSITIVE | FALSE_POSITIVE | CANNOT_VERIFY
  CONFIDENCE: <0-10>
  REFUTE_REASON: <doesnt_exist|already_handled|implausible_trigger|
    intentional_behavior|misread_code|duplicate|not_actionable|n/a>
  EXCLUSION_RULE: <1-16, org rule, or none>
  FIRST_LINK: <file:line or "none found">
  RATIONALE: <2-5 sentences, file:line cited>

FINDING: {id} {file}:{line} {category} (claimed {severity})
{title}
{description}
Vote {k}/{N}. Independent; do not seek other votes.

{"phase": 3, "context": {...}, "findings": [ {all findings with verdict/vote_breakdown/confidence/refute_reasons/first_links/rationale/exclusion_rule} ], "confirmed": ["f001", "..."]}

You are assigning severity to a CONFIRMED security finding. Verification
already happened; assume the finding is real. Your only job is to derive
how bad it is, independently of what the scanner claimed.

You may Read/Grep the codebase at {REPO_PATH} to check preconditions. Do
NOT execute code.

ENVIRONMENT: {context.environment}
THREAT MODEL (operator-stated, may be empty):
{context.threat_model as bullets, or "(none provided)"}
SCORING STANDARD: {context.scoring}

FINDING:
  id:        {id}
  file:      {file}:{line}
  category:  {category}
  claimed severity: {severity}
  reachability evidence: {first_links from Phase 3}
  verifier rationale: {rationale from Phase 3}

────────────────────────────────────────────────────────────────────────
STEP 1: Enumerate EVERY precondition that must hold for exploitation.
Be concrete: required auth state, configuration, prior request, race
window, attacker position. Then state the minimum ACCESS LEVEL required
(unauthenticated remote / authenticated / local / physical).

STEP 2: Derive severity from the precondition count and access level:

  | Preconditions | Access required          | Severity |
  |---------------|--------------------------|----------|
  | 0             | Unauthenticated remote   | HIGH     |
  | 1-2           | Authenticated            | MEDIUM   |
  | 3+            | Local-only / no demo path| LOW      |

  Evaluate each column independently and take the LOWER result. Example:
  0 preconditions but authenticated-only is MEDIUM, not HIGH; 1
  precondition but local-only is LOW. Cross-check: if your preconditions
  list has 3+ items, HIGH is almost certainly wrong.

STEP 3: Threat-model match. If the THREAT MODEL is non-empty and this
finding maps onto one of its entries, note which one. A match may raise
severity by ONE step (LOW to MEDIUM or MEDIUM to HIGH), never two. If the
threat model is empty, skip this step.

STEP 4: Judge the scanner's claimed severity. From the perspective of an
engineer who has reviewed two hundred scanner findings this week and is
allergic to inflation: would the CLAIMED severity contribute to alert
fatigue? Is it comparable to a real CVE at that level? Is the code in test
fixtures or dev-only config? Score in -5..+5:
  +3..+5  claimed severity is justified or understated
   0..+2  roughly right
  -1..-3  inflated by one level
  -4..-5  badly inflated (LOW dressed as HIGH)

STEP 5: verify_verdict. Exactly one of:
  exploitable        preconditions are realistically satisfiable
  mitigated          real, but a deployed control reduces it below the
                     derived severity (name the control)
  needs_manual_test  severity hinges on something only a runtime test can
                     settle; recommend a human build a PoC

STEP 6: If SCORING STANDARD is a CVSS or OWASP variant, emit a
`severity_label` in that format (vector string + base score for CVSS;
likelihood x impact for OWASP). Otherwise set it equal to the derived
HIGH/MEDIUM/LOW.

────────────────────────────────────────────────────────────────────────
Respond with ONLY this block:

  PRECONDITIONS:
  - <one per line>
  ACCESS_LEVEL: <unauthenticated_remote|authenticated|local|physical>
  SEVERITY: <HIGH|MEDIUM|LOW>
  SEVERITY_LABEL: <per scoring standard>
  THREAT_MATCH: <matched threat-model entry, or none>
  SEVERITY_ALIGNMENT: <-5..+5>
  VERIFY_VERDICT: <exploitable|mitigated|needs_manual_test>
  RANK_RATIONALE: <2-4 sentences>

{"phase": 4, "context": {...}, "findings": [ {all findings with severity/severity_label/preconditions/access_level/threat_match/severity_alignment/verify_verdict} ]}

{"phase": 5, "context": {...}, "findings": [ {all findings with owner_hint} ]}

{
  "triage_completed": true,
  "triage_context": {
    "mode": "interactive|auto",
    "environment": "...",
    "threat_model": ["..."],
    "scoring": "...",
    "noise_tolerance": "...",
    "votes_per_finding": 3,
    "repo": "..."
  },
  "summary": {
    "input_count": 0,
    "duplicates": 0,
    "false_positives": 0,
    "true_positives": 0,
    "needs_manual_test": 0,
    "by_severity": {"HIGH": 0, "MEDIUM": 0, "LOW": 0}
  },
  "findings": [
    {
      "id": "f001",
      "source": "VULN-FINDINGS.json#0",
      "title": "...",
      "file": "...",
      "line": 0,
      "category": "...",
      "claimed_severity": "HIGH",
      "verdict": "true_positive|false_positive|duplicate",
      "verify_verdict": "exploitable|mitigated|needs_manual_test|null",
      "confidence": 0.0,
      "severity": "HIGH|MEDIUM|LOW|null",
      "severity_label": "...",
      "severity_alignment": 0,
      "preconditions": ["..."],
      "access_level": "...",
      "threat_match": "...|null",
      "rationale": "file:line-cited prose: reachability, protections, why each held or didn't; then ranking rationale",
      "vote_breakdown": {"true_positive": 0, "false_positive": 0, "cannot_verify": 0},
      "refute_reasons": ["..."],
      "exclusion_rule": null,
      "first_links": ["file:line", "..."],
      "duplicate_of": null,
      "absorbed": ["..."],
      "owner_hint": "...",
      "missing_fields": ["..."]
    }
  ]
}

undefined

**Step 2 — per finding.** For each true_positive in severity order:
1. Write tool → `./.triage-state/_chunk.tmp` containing ONE finding's section:

2. Bash:
   `python3 .claude/skills/_lib/checkpoint.py append ./TRIAGE.md --from ./.triage-state/_chunk.tmp`

Repeat for each true_positive.

**Step 3 — footer.** Write tool → `./.triage-state/_chunk.tmp` containing the
Dropped table, then `checkpoint.py append` it the same way:

**Checkpoint (final):** Bash:
`python3 .claude/skills/_lib/checkpoint.py done ./.triage-state 6`
The next invocation's resume check sees `status == "complete"` and starts
fresh.

Triage complete: {N} findings -> {T} confirmed, {F} false positives, {D} duplicates.

  HIGH:   {n}   {title of top HIGH, owner_hint}
  MEDIUM: {n}
  LOW:    {n}
  Needs manual test: {n}

  Top refute reasons: {top 3 refute_reasons with counts}

Wrote ./TRIAGE.md and ./TRIAGE.json

/triage .claude/skills/triage/fixtures/canary-findings.json --auto --repo targets/canary

vuln-pipeline run drlibs --runs 3 --parallel --stream
/triage results/drlibs/<ts>/ --repo targets/drlibs