T3MP3ST public launch build
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# T3MP3ST CVE-Replay Hunt — v1
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Autonomous code-audit benchmark for measuring t3mp3st's (or any LLM agent's)
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ability to rediscover real CVEs from source-code-only context. Drives a hunter
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+ optional SPHINX-style judge against a corpus of small source files — each
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either a faithful reproduction of a public CVE or a hardened clean reference —
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and scores every finding through 5 hard validation gates plus a decoy
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false-positive penalty.
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**This is NOT keyword bingo.** A finding only counts as a real detection when
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the hunter pins the right file, declares an acceptable CWE class, points near
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the actual sink line, uses the right terminology, AND produces an exploit
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primitive matching the documented pattern. The SPHINX judge then second-passes
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each finding and can boost (good evidence) or halve (non-exploitable claim)
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the awarded points.
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## Layout
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```
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bench/cve-hunt/
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├── manifest.yaml corpus index + scoring rules
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├── samples/ 10 real CVEs + 2 decoys
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│ ├── CVE-2014-6271/ Shellshock (bash, eval env)
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│ ├── CVE-2014-0160/ Heartbleed (C, OOB read)
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│ ├── CVE-2021-44228/ Log4Shell (Java, JNDI subst)
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│ ├── CVE-2018-1000156/ GNU patch (C, cmd inj)
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│ ├── CVE-2017-9805/ Struts2 XStream (Java, deser)
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│ ├── CVE-2017-5638/ Struts2 S2-045 (Java, OGNL)
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│ ├── CVE-2019-11043/ PHP-FPM (C, OOB write)
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│ ├── CVE-2020-1472/ Zerologon (C, fixed IV)
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│ ├── CVE-2022-22965/ Spring4Shell (Java, classloader)
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│ ├── CVE-2019-19781/ Citrix Shitrix (Perl, path trav)
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│ ├── DECOY-clean-c/ hardened C copy
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│ └── DECOY-clean-java/ hardened XStream binder
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└── results/ JSON reports (gitignored)
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```
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Each sample directory has `source.<ext>` plus `ground-truth.yaml` declaring:
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- `cve` / `alias` — identity (NOT shown to the hunter)
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- `cwe_primary` + `cwe_acceptable` — CWE families that count as a hit
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- `vuln_lines` — sink locations (line proximity gate: ±5)
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- `exploit_pattern_regex` — regex the hunter's PoC must match
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- `must_mention_any` — terminology that should appear in the description
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## Scoring (per finding)
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| Gate | Weight | Rule |
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| ----------------- | ------ | ------------------------------------------------------------ |
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| `file_match` | 1 | reported file == `ground_truth.target_file` |
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| `cwe_match` | 2 | reported CWE ∈ `cwe_acceptable` |
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| `line_proximity` | 1 | reported line within ±5 of any `vuln_lines` |
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| `keyword_match` | 1 | ≥2 of `must_mention_any` appear in `title+description` |
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| `exploit_pattern` | 1 | `exploit_pattern_regex` matches reported PoC |
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Max per real CVE = **6**. Net = `(best_score / 6) * cve.points`.
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A finding **detects** only when it hits the same file AND
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(matches CWE or hits line-proximity). Anything else is noise.
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**Decoy penalty:** -5 points per false positive against a clean sample.
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With `--judge`, the judge can catch a decoy FP (verdict `valid_vulnerability: false`)
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which halves the penalty (incentivizes confident decoy rejection).
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### Judge pass (SPHINX-style)
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When `--judge` is on, each finding is also reviewed by a second LLM (or
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deterministic stub) returning:
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```
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{ valid_vulnerability, judge_confidence, severity_assessment,
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evidence_quality: 0..3, disagreements, reasoning }
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```
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The judge modifies scoring:
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- `valid_vulnerability: true` → points × (1 + evidence_quality/3 × 0.25), capped at full points
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- `valid_vulnerability: false` → points × 0.5 (structural gates passed but judge says non-exploit)
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- Decoy FP caught by judge → penalty halved (`5` → `2.5`)
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## Modes
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| Mode | Hunter | LLM key | Purpose |
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|------|--------|---------|---------|
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| `--stub` (default) | canned correct findings | none | Validator dry-run — proves gates+scoring real |
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| `--adversarial` | canned wrong findings | none | Proves validator catches bad findings |
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| `--live` | direct LLM call (OpenRouter/Anthropic/OpenAI) | yes | Benchmarks raw model on code audit |
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| `--orchestrated` | local t3mp3st `/api/general/auto` | yes | Benchmarks t3mp3st platform pipeline |
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## Configuring your LLM key
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Drop your key in `.env` at the t3mp3st repo root — it's already gitignored,
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auto-loaded by both the bench and the t3mp3st server.
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```bash
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# Pick one (provider auto-detects in order: OpenRouter → Anthropic → OpenAI)
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echo 'OPENROUTER_API_KEY=sk-or-v1-...' >> .env
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# or
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echo 'ANTHROPIC_API_KEY=sk-ant-...' >> .env
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# or
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echo 'OPENAI_API_KEY=sk-...' >> .env
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```
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You can also run the interactive helper: `npm run setup:api`.
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Force a provider with `--provider openrouter|anthropic|openai`. The bench
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normalises model IDs per provider (e.g. `claude-opus-4-7` →
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`anthropic/claude-opus-4-7` for OpenRouter).
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## Usage
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```bash
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# Validator dry-run (default):
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npm run cve:bench
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# With SPHINX-style judge boost (recommended):
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node scripts/cve-hunt-bench.mjs --judge
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# Adversarial sanity check (expect net_score < 0):
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npm run cve:bench:adversarial
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# Real Claude run:
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ANTHROPIC_API_KEY=sk-ant-... npm run cve:bench:live
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# Full t3mp3st orchestrated hunt (requires t3mp3st server running):
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ANTHROPIC_API_KEY=sk-ant-... npm run server &
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ANTHROPIC_API_KEY=sk-ant-... node scripts/cve-hunt-bench.mjs \
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--orchestrated --judge \
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--report bench/cve-hunt/results/orchestrated.json
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# Single sample, custom model:
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node scripts/cve-hunt-bench.mjs --live --judge \
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--sample CVE-2021-44228 --model claude-opus-4-7 \
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--judge-model claude-sonnet-4-5 \
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--report bench/cve-hunt/results/log4shell.json
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```
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The orchestrated mode auto-handles t3mp3st's autonomous-execution approval
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gate: it captures the 403's `approval.id`, posts to `/api/approvals/:id/approve`,
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sets `body.approvalId`, and retries. Up to 4 nested approvals are handled
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(autonomous_execution → mission_execution per target).
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## Calibration (current corpus = 10 CVEs + 2 decoys, max points = 295)
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| Mode | Detect | FP | F1 | Net Score | Score % |
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| ------------------------ | ------ | -- | ---- | --------- | -------- |
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| `--stub` | 10/10 | 0 | 1.00 | 285 | 96.6% |
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| `--stub --judge` | 10/10 | 0 | 1.00 | 293.4 | **99.5%** |
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| `--adversarial` | 0/10 | 2 | 0.00 | -10 | -3.4% |
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| `--adversarial --judge` | 0/10 | 2 | 0.00 | -4 | -1.4% |
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The 0.5% gap on stub+judge is Heartbleed and Zerologon stubs — both describe
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the attack in natural language rather than dropping a literal payload, so the
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`exploit_pattern` regex misses. The judge boosts them via `evidence_quality=2`
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but can't push to 100% without the pattern hit. This is the bench correctly
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rewarding concrete reproducible PoCs over hand-wavy descriptions.
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## Extending the corpus
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Add `samples/CVE-XXXX-YYYY/` with `source.<ext>` + `ground-truth.yaml`, then
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register under `samples:` in `manifest.yaml`. If using `--stub` mode, also add
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a stub entry to `stubHunter()` in `scripts/cve-hunt-bench.mjs`. Difficulty
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labels (`easy/medium/hard/decoy`) are informational; scoring is gate-driven.
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## Roadmap
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- [ ] Behavioural validation (sandboxed PoC execution against vulnerable container)
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- [ ] Replay against actual vulnerable releases (Docker corpus)
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- [ ] Multi-judge cross-validation (sonnet vs. opus disagreement signal)
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- [ ] CVSS-weighted scoring
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- [ ] t3mp3st code-audit operator family (so orchestrated mode has tooling)
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- [ ] Per-CWE-family heat-map output for systematic blind-spot detection
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