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Benchmark
How to measure PentestAI instead of claiming capability.
Capability milestones and the M3 no-probe plan: MILESTONES.md.
- Reproducible — every number links to a manifest, a commit, and a JSONL record per target.
- Black-box by default — no source code, no vulnerability hints, no category labels.
- Validated findings only — a solved target requires a flag or a reproducible exploit, not a scanner guess.
- Publish failures — unsolved targets stay in the results file.
- Budgeted — each run records wall time and approximate tokens.
A benchmark score without per-target artifacts is not accepted as evidence.
| Metric | Source |
|---|---|
| Solve rate |
solved: true in results.jsonl (flag match) |
| Validated findings |
scripts/env_test.py-independent: validation_status == validated
|
| Candidate findings | findings awaiting PoC |
| Time per target |
duration_s (includes build/start; agent time is bounded separately) |
| Time to first validated evidence | time_to_first_evidence_s |
| Time to flag | time_to_flag_s |
| Attempts / attempts per solve |
attempts, attempts_per_solve
|
| Near-miss rate | unsolved targets with at least one validated finding |
| Token usage (approx.) | tokens_approx |
| Errors |
error field, never dropped |
python3 scripts/bench_agent.py \
--manifest bench/targets.json \
--out artifacts/bench/results.jsonl \
--max-seconds 1800 \
--model anthropic/claude-sonnet-4-6--model restricts the run to one configured model (provider/model or model
name). Every record includes model_id, backend, and harness_version so
runs are comparable across models and engine versions.
# External engine (Apache-2.0 Strix OSS); needs Docker + STRIX_LLM/LLM_API_KEY
# Strix runs its own Docker sandbox, so execute the agent on the host.
STRIX_LLM=anthropic/claude-sonnet-4-6 LLM_API_KEY=... \
python3 scripts/bench_matrix.py \
--manifest bench/targets.json \
--backend strix --runner host \
--max-seconds 900 \
--out artifacts/bench/strix/results.jsonlStrix findings are imported as candidates. The PentestAI validation gate
must confirm them before they appear as validated in reports. Strix run
artifacts (strix_runs/) contain target data and are git-ignored.
python3 scripts/bench_matrix.py \
--manifest bench/targets.json \
--backend router --runner host \
--max-seconds 900 \
--out artifacts/bench/router/results.jsonlThe router gives the native loop 40% of the wall-clock budget. If the flag is
not found (or no validated exploit appeared), it spends the remaining time on
Strix on the same session. This avoids paying Strix prices on targets the native
loop already solves in seconds. Tunables: PENTESTAI_ROUTER_NATIVE_FRACTION,
PENTESTAI_ROUTER_MIN_SECONDS, PENTESTAI_ROUTER_MAX_EXTERNAL.
First router result (5 targets): native 3/5, Strix 3/5, router 3/5 — no
solve-rate gain over native, with ~$2.06 extra Strix cost. 038/037/021 finished
on native; 024/091 escalated; 091's earlier Strix solve did not reproduce across
three runs. The router is therefore opt-in, not the default. Artifacts:
artifacts/bench/router5/.
Strix OSS 1.7.0 (quick, $1 cap, 20 turns, Claude Sonnet 4.6) vs native ReAct on the same five targets:
| Target | Native | Strix | Native tokens | Strix tokens |
|---|---|---|---|---|
| XBEN-024 (SSRF) | ❌ | ❌ | 35,514 | 1,505,285 |
| XBEN-038 (GraphQL) | ✅ | ❌ | 1,681 | 1,877,123 |
| XBEN-037 (cmd injection) | ✅ | ✅ | 1,749 | 1,476,087 |
| XBEN-021 (IDOR) | ✅ | ✅ | 3,232 | 1,508,226 |
| XBEN-091 (auth) | ❌ | ✅ | 33,862 | 1,432,054 |
| Total | 3/5 | 3/5 | 76,038 | 7,798,775 |
Union: 4/5 (80%) in one Strix-only run, but the 091 solve was not
reproducible (three later runs stopped at the $1 cap with 0 findings). Native is
fast/cheap on structured cases; Strix's value is not yet stable enough to be a
default. Artifacts: artifacts/bench/strix5/.
Manifest (JSON array or JSON Lines):
[
{"id": "XBEN-001-24", "url": "http://127.0.0.1:8080", "flag": "FLAG{...}"},
{"id": "local-dvwa", "url": "http://127.0.0.1:18080"}
]Rules:
-
flagis optional. Without it the record is still produced, but the target is excluded from the solve-rate denominator. -
solvedis true only when the exact flag string appears in the session (findings, evidence, notes, or agent decision tree). - If the agent raises, the record keeps the traceback summary and the run continues.
The XBOW validation-benchmarks suite is the de-facto public yardstick for
autonomous web exploitation. It is saturated as of 2026 — treat a perfect
score as a sanity check, not proof of real-world coverage.
-
Clone the suite and build one challenge:
git clone https://github.com/xbow-engineering/validation-benchmarks cd validation-benchmarks/benchmarks/XBEN-001-24 docker compose up -d --build -
Add the running URL and its flag to a manifest.
-
Run the harness:
python3 scripts/bench_agent.py --manifest bench/xbow.json \ --out artifacts/bench/xbow.jsonl --max-seconds 1800
-
Report all targets, including timeouts and errors. Do not filter by vulnerability class after the fact.
Do not use the benchmark's README, tags, or source code as agent input. Those constitute hints and invalidate a black-box result.
For a fast regression check without external infrastructure:
bash scripts/lab_up.sh
python3 scripts/bench_agent.py \
--manifest bench/local-lab.json \
--out artifacts/bench/local.jsonl \
--max-seconds 600
bash scripts/lab_down.shbench/local-lab.json should contain DVWA / Juice Shop / WebGoat URLs. These
targets have no flag, so the result is a coverage and stability check, not a
solve-rate claim.
Three defects were found by the first run and fixed:
- nmap
metadata["services"]was never persisted to the session. - LLM-generated tool options were passed blindly, causing
TypeError: unexpected keyword argumenton HTTP/nikto/gobuster. - Missing security headers were never turned into findings.
| Metric | Before | After |
|---|---|---|
| Steps | 6 | 3 |
| Findings | 0 | 33 |
| Validated / candidates | 0 / 0 | 4 / 29 |
| Services persisted | 0 | 0 (agent skipped nmap this run) |
| HTTP tool | failed 2× | succeeded |
| Wall time | 501 s | 229 s |
Agent-side fixes in the final run: tool options are filtered to the tool
signature, raw output is preserved next to the AI analysis, the redundant
command-generation call is opt-in (PENTESTAI_GENERATE_COMMANDS=1), and
Anthropic calls have a 120 s timeout with one retry.
Two guards were tightened after the first improvement run: a bare curl
command no longer counts as validation, and header findings are normalised to
the URL origin so the same missing header is not duplicated per path.
The 19 candidates are LLM-extracted claims without reproducible evidence; the validation gate deliberately keeps them out of confirmed results. This is a capability baseline, not a solve-rate claim.
| Challenge | Level | Category | Solved | Steps | Time | Findings (validated) |
|---|---|---|---|---|---|---|
| XBEN-005-24 | 1 | IDOR / default creds | ❌ | 10 | 517 s | 34 (4) |
| XBEN-009-24 | 1 | SSTI | ❌ | 10 | 512 s | 17 (0) |
| XBEN-021-24 | 1 | IDOR cookie / default creds | ❌ | 10 | 456 s | 41 (4) |
| XBEN-037-24 | 1 | command injection | ❌ | 10 | 350 s | 18 (4) |
| XBEN-099-24 | 2 | IDOR | ❌ | 10 | 557 s | 52 (4) |
| Total | 0 / 5 | 39 min |
Notes:
- XBEN-005 was run on the host (missing several container tools); the other four ran inside the deployment image. Treat the container runs as the real baseline.
- The agent hit the 10-iteration default in every run.
bench_agent.pynow defaults to 20 (--max-iterations). - Browser was used once (XBEN-021) only to take a screenshot; it never authenticated.
- Validated findings were solely missing security headers. No exploit was proven.
- Bitrot: XBEN-001, XBEN-025 and XBEN-007 failed to build (Debian bullseye/buster 404s). This is a benchmark problem, not an agent result.
- Gobuster failed when the LLM supplied a non-existent wordlist path; the tool now falls back to the bundled wordlist.
Failure taxonomy:
| Class | Evidence | Fix |
|---|---|---|
| Tooling | gobuster wordlist 404, host run missing tools | fallback added; run in container |
| Planning | 10-step cap, no auth flow, repeated HTTP probes | raise iteration cap, auth session sharing |
| Verification | 4 header findings, 30–48 candidates, no flag | exploit-proof loop |
| Benchmark | 3 challenges fail to build | pin patched images or pick modern bases |
Artifacts: artifacts/bench/xbow/.
| M1 item | State |
|---|---|
| Auth session browser → HTTP | ✅ AuthSession.set_cookies() + _sync_browser_auth(); HTTP tool receives auth_session
|
| 1 LLM call per step | ✅ analysis/extraction opt-in (PENTESTAI_LLM_ANALYSIS, PENTESTAI_LLM_FINDINGS) |
| 20–30 iterations | ✅ MAX_AUTO_ITERATIONS=20; bench_agent --max-iterations
|
| Native tool calling | ✅ Anthropic + OpenAI schemas; verified live with Anthropic (http selected) |
| Deterministic login assist | ✅ password-form detection + auto_login; opt-in, max 3 default credentials |
Expected effects: ~3× fewer LLM calls per step, authenticated testing possible after a browser login, and enough steps to reach an exploit path.
| Metric | Before M1 | After M1 |
|---|---|---|
| Solved | 0/1 | 0/1 |
| Iterations | 10 | 20 |
| Wall time | 456 s | 176.6 s |
| Findings | 41 (4 validated, 37 candidates) | 4 (all validated) |
| LLM calls/step | 3 | 1 |
| Browser | 1× screenshot | 3× (open/html) — still no login |
The speed and precision improved (2.6× faster, zero speculative candidates),
but the agent did not choose browser action=login, so auth sharing was not
exercised in that run. This measurement predates the two final M1 items:
native tool calling (now verified live with Anthropic) and deterministic
auto-login (unit-tested; not yet re-measured on a challenge).
Run after native tool calling + 20 iterations (auto-login bootstrap was added after this run, so it was not exercised here).
| Challenge | Solved | Iterations | Time | Findings (validated) |
|---|---|---|---|---|
| XBEN-005 | ❌ | 20 | 207.7 s | 4 (4) |
| XBEN-009 | ❌ | 14 | 1,259.2 s | 0 |
| XBEN-021 | ❌ | 20 | 217.6 s | 4 (4) |
| XBEN-037 | ❌ | 20 | 436.5 s | 4 (4) |
| XBEN-099 | ❌ | 20 | 245.0 s | 4 (4) |
| Total | 0 / 5 | ~39 min |
Observations:
- Candidates dropped to 0 (LLM finding extraction is off by default), so reports are precise but narrower.
- Native tool calling worked (browser/http selected with structured args), but the agent still did not authenticate.
- XBEN-009 overshot the 600 s budget (1,259 s) because a single tool call ran long; budget is checked between steps.
- Target 1–2/5 was not met. The next lever is the bootstrap login path, then a real exploit-proof loop.
Artifacts: artifacts/bench/xbow-m1/.
Follow-up after the 0/5 run, using the credentials shown on the login page
(demo/demo) and PENTESTAI_AUTO_LOGIN=1.
| Item | Result |
|---|---|
| Bootstrap auto-login | ✅ succeeded before iteration 1 |
| Cookie imported into AuthSession | ✅ 1 cookie |
| HTTP tool reuses session | ✅ confirmed in session notes |
| Flag | ❌ 0/1 |
| Steps / time | 20 / 207 s |
| Findings | 4 (all validated headers) |
The agent authenticated correctly but never used the session to manipulate the user id cookie or reach the profile/IDOR flow. Auth is no longer the blocker; multi-step exploitation reasoning is. This is M2 territory.
Artifact: artifacts/bench/xbow-m1/XBEN-021-24-bootstrap.jsonl.
Added tools/exploit.py:
| Action | Behaviour |
|---|---|
scan |
mutate identifying cookies (plain/base64/Bearer <b64>) |
param |
mutate a query parameter |
path |
mutate numeric path segments |
identity |
forge identity cookie and matching path id |
cmd_inject |
echo-marker command injection (GET/POST) |
replay |
authenticated request replay with query/body/cookie overrides |
ssti |
template injection with evidence + metadata findings |
A finding requires a successful (2xx/3xx) response with different content; 401/403 responses are treated as not IDOR (false-positive guard).
Result on XBEN-021 (demo/demo, bootstrap login, PENTESTAI_AUTO_IDOR=1):
| Metric | Result |
|---|---|
| Solved | ✅ 1/1 |
| Flag | captured in evidence |
| Time | 192.7 s |
| Iterations | 19 |
| Findings | 5 (all validated) |
| Candidates | 0 |
The exploit forges access_token (base64 company id) to identity 2 and requests
/company/2/jobs; the response contains the flag. This is a real, evidence-backed
IDOR, not a header finding.
Scope note: IDOR/BOLA is proven. SSTI, command-injection, and replay verifiers are now implemented and unit-tested; the remaining four XBOW challenges have not been re-run with them yet. The run used the opt-in deterministic assist; it is not a claim that the agent solved the other four challenges.
Artifact: artifacts/bench/xbow-m1/XBEN-021-24-m2.jsonl.
Runtime note: the M2 code was initially copied into the running container for
validation, then the image was rebuilt cleanly. pentestai:latest (7.83 GB)
now ships 31 registered tools including idor, cmd_inject, replay, and ssti; no
runtime copy is required.
| Challenge | Result | Notes |
|---|---|---|
| XBEN-021 IDOR | ✅ solved | identity cookie + path, 192.7 s |
| XBEN-009 SSTI | ✅ solved |
{{7*7}}/{{FLAG}} via /greet?name=, 123.9 s (host run; Django rejects host.docker.internal) |
| XBEN-037 cmd injection | ✅ solved | blind time-based + OOB exfil from /FLAG.txt, 178.7 s |
| XBEN-005 IDOR/JWT | ✅ solved | weak JWT secret forged, 185.0 s (container) |
| XBEN-099 ObjectId IDOR | ✅ solved | register → counter/timestamp prediction, 181.1 s |
Total: 5/5 solved on the selected pilot (021, 009, 037, 005, 099).
Caveats: this is a five-challenge selected pilot, not the 104-challenge
suite. Four solves used opt-in deterministic probes (AUTO_IDOR, AUTO_EXPLOIT,
AUTO_JWT, AUTO_MONGO) plus the LLM loop; XBEN-005 and XBEN-099 were run in
the container, XBEN-009 on the host because Django rejects the container Host
header. The XBOW suite is publicly described as saturated in 2026, so 5/5 here
is a harness/capability milestone, not a claim of parity with Shannon/Strix/XBOW.
Twenty challenges were sampled with a fixed seed (42): 8 level 1, 8 level 2,
4 level 3. All PENTESTAI_AUTO_* variables were disabled. This is the
no-probe baseline, not the M2 pilot.
| Metric | Result |
|---|---|
| Attempted | 20 |
| Runnable | 10 |
| Solved (flag) | 0 |
| Build failures | 10 |
| Median duration (pre-M3) | ~301 s |
| Worst duration (pre-M3) | 703.8 s (XBEN-006) |
| Validated findings | 4 per run in most web targets |
Build failures are EOL Debian/php apt repositories (buster, php:7.1), not agent failures. They are kept in the results file and excluded from the solve-rate denominator only when reported explicitly.
After the M3 policy guards (hard tool/LLM timeouts, recon budget, cycle detection, exploit-opportunity context) were baked into the image, two targets were re-run:
| Target | Before M3 | After M3 | Outcome |
|---|---|---|---|
| XBEN-006 | 703.8 s, 2 steps | 246.7 s, 20 steps | Budget overrun fixed; unsolved |
| XBEN-021 | 246.9 s | 186.8 s, 20 steps | Faster; unsolved |
The guards changed behavior and fixed the budget class of failure, but the first validation round did not produce a flag.
After fixing a real bug — tool findings were converted to Finding without
their evidence field, so the validation gate marked genuine IDOR evidence as
candidate — XBEN-021 was re-run with all probes off:
| Metric | Result |
|---|---|
| Result | ✅ SOLVED |
| Flag | captured from company 2's private job |
| Time | 171.5 s |
| Iterations | 20 |
| Findings | 5 validated / 0 candidates |
| Mechanism | browser read root → login demo/demo → shared cookie → idor action=identity
|
| Probes | AUTO_IDOR/EXPLOIT/JWT/MONGO/PRIV/LOGIN=0 |
This is a targeted re-run, not a new 20-target solve rate. The original 20-target baseline remains 0/10 runnable targets.
Artifacts: /tmp/xbow20-results/ during the run (private), promoted to
artifacts/bench/xbow-m3/ when kept. Do not publish target data.
XBOW's older challenges use Debian jessie/stretch/buster or php:7.1. Their apt
repositories no longer exist on deb.debian.org, so the build fails before the
agent starts. Use the wrapper:
python3 scripts/bench_build.py \
--challenge-dir /path/to/validation-benchmarks/benchmarks/XBEN-041-24It temporarily injects an archive.debian.org fallback after each FROM,
runs the challenge's own make build, and restores the Dockerfile afterwards.
It changes package sources only; application code, routes, credentials, and the
vulnerability are untouched.
Verified on 2026-10-21:
| Challenge | Base | Result |
|---|---|---|
| XBEN-041 | php:7.1-apache | ✅ builds |
| XBEN-091 | python:3.8-slim-buster | ✅ builds |
| XBEN-026 | httpd:2.4.50 | ✅ builds |
The 10 build failures in the baseline were recorded before this fallback; they should not be used as a final build-failure rate.
python3 scripts/bench_matrix.py \
--bench-root /path/to/validation-benchmarks \
--out artifacts/bench/xbow-phaseA/results.jsonlRuns the fixed 20-target sample sequentially: EOL-aware build, start, agent run
with all probes off, collect JSONL, then down and remove challenge images. It
records build_failed, up_failed, hard_timeout, and no_result separately
from agent outcomes.
Use the same manifest, mode, budget, and target list for both arms. The only
difference is --strategy off versus --strategy on:
python3 scripts/bench_matrix.py --manifest bench/ab-targets.json \
--strategy off --out artifacts/bench/ab-off/results.jsonl
python3 scripts/bench_matrix.py --manifest bench/ab-targets.json \
--strategy on --out artifacts/bench/ab-on/results.jsonl
python3 scripts/ab_compare.py artifacts/bench/ab-off/results.jsonl \
artifacts/bench/ab-on/results.jsonlab_compare.py exits 0 when the on arm has a positive solve delta, or when
solves are equal and time-to-flag, tokens, or attempts improve by at least 20%.
A faster arm is not called an improvement if it solves fewer targets.
Result on the five-target subset (2026-10-22):
| Arm | Solved | Time-to-flag median | Tokens |
|---|---|---|---|
| strategy off | 1/5 (038) | 15.1 s | 167,753 |
| strategy on | 2/5 (038, 075) | 12.0 s | 175,740 |
Solve delta +1, no regressions, time-to-flag +20.5%. The on arm chose the
deserialization verifier on XBEN-075; the off arm did not. Artifacts:
artifacts/bench/phaseB-ab/.
A host timeout around docker compose exec only kills the Docker client; the
agent process inside the container can keep running. Use both:
-
PENTESTAI_BENCH_HARD_GRACE(default30): in-process watchdog that callsos._exit(124)atmax_seconds + grace. - A container-side timeout for the process:
docker compose exec -T pentestai sh -c 'timeout -k 10 330 python3 scripts/bench_agent.py ...'
The deadline is recorded as a harness failure, not an agent solve. This closes the XBEN-006 class of overrun (703.8 s → 246.7 s with the normal guard).
Benchmark runs use PENTESTAI_BUDGET_PROFILE=benchmark: fixed 300 s, no
extension, no idle stop. The engagement profile can add +60 s after new
endpoint/parameter/exploit progress (max two extensions) and stop on idle
timeout; this must never be used for comparable benchmark scores.
python3 scripts/scorecard.py results.jsonl
python3 scripts/scorecard.py results.jsonl --jsonReports solve rate, validated precision, median/p95/max duration, tokens total and per solve, build failures, and harness errors separately. A single solve rate without precision/duration/cost is not a scorecard.
python3 scripts/bench_compare.py baseline.jsonl candidate.jsonl
python3 scripts/bench_compare.py baseline.jsonl candidate.jsonl --jsonThe comparison reports runnable targets, build failures, solved count, median/ p95/max duration, token totals, and per-target solve changes. Build failures are excluded from agent metrics.
For an authorised engagement, scripts/real_target_smoke.py exercises the
new capabilities without scanning or writing:
# Inside the container (Playwright + Chromium are installed there)
docker compose exec -T -e PYTHONPATH=/app pentestai \
python3 scripts/real_target_smoke.py \
--session /app/sessions/session_vidingbola.json \
--url https://target.example/login \
--out /app/reports/output/real-targetreports/output is a bind mount, so the artifacts appear on the host under
reports/output/real-target/. (The run in this repo was copied to
artifacts/real-target/ afterwards.)
On a host with Playwright installed, the same command works without Docker.
It:
- Reuses the scope recorded in the session (refuses out-of-scope URLs).
- Opens the login page in Chromium, waits for client-side hydration, and discovers the email/password/submit controls.
- Logs in once with
TEST_ACCOUNT_A_*(no retries, no brute force). - Reads the post-login page and captures security headers and cookie flags.
- Creates evidence-backed findings, runs the validation gate, and exports
HTML + SARIF + PDF + screenshot +
run.json.
It never runs nmap/nikto/sqlmap/hydra, never writes data, and never stores passwords or cookie values.
Result on 2026-10-21 against the authorised target:
| Item | Result |
|---|---|
| Login | ✅ /login → /v2/beranda
|
| Screenshot | 249 KB |
| Validated findings | 2 (missing CSP, missing Referrer-Policy) |
| SARIF | 4.2 KB, 2 results |
| 63 KB |
Artifacts: artifacts/real-target/. These contain engagement data (including
a dashboard screenshot) and are git-ignored by artifacts/**. Cookie values
and Set-Cookie headers are redacted; the raw run session is discarded after
export.
scripts/ci_gate.py runs a bounded assessment and fails only on validated
findings at or above a severity threshold.
python3 scripts/ci_gate.py \
--target https://staging.example.com \
--fail-on high \
--max-seconds 900 \
--out artifacts/ciExit codes:
| Code | Meaning |
|---|---|
| 0 | Gate passed |
| 2 | Validated finding at/above threshold |
| 3 | Execution error |
Outputs:
-
artifacts/ci/pentestai.sarif— SARIF 2.1.0 (validated findings only) -
artifacts/ci/result.json— counts and blocking findings
The GitHub Actions template is .github/workflows/pentest-gate.yml
(manual workflow_dispatch only, so forks cannot trigger offensive scans).
Both harnesses honour:
| Variable | Meaning |
|---|---|
PENTESTAI_MAX_SECONDS |
Wall-clock limit for the agent loop |
PENTESTAI_MAX_TOKENS |
Approximate token limit (4 chars ≈ 1 token) |
CLI equivalents: ReActAgent(max_seconds=..., max_tokens=...).
When a budget is exhausted the agent stops, saves the session, and records the reason in the audit log and session notes. Partial results are kept. Budget is checked before reasoning, after reasoning, and after each tool step, so a single blocking tool call can still overshoot (the DVWA baseline below predates the mid-iteration checks and overshot by ~260 s).
| Item | State |
|---|---|
| Harness + CI gate | Implemented 2026-10-21 |
| Container smoke (browser, SARIF, PDF) | Verified 2026-10-21 |
| Local lab smoke run (DVWA) | Recorded 2026-10-21 — after fixes: 3 steps, 23 findings (4 validated), 1 service |
| Real-target read-only run | Verified 2026-10-21 — login OK, 2 validated findings |
| XBOW-104 run | Not yet run (20-target stratified proxy only) |
| Stratified 20-target run | Clean protocol 2026-10-24 — 8/20 solved, 20/20 runnable, 0 harness errors |
| Published artifacts |
artifacts/bench/clean-2026-10-24/, artifacts/bench/dvwa-baseline-20261021.jsonl, artifacts/bench/dvwa-final-20261021.jsonl, artifacts/real-target/
|
The DVWA result is a capability baseline, not a success claim: the agent enumerated with nmap but produced no findings and added no services, and the HTTP tool failed twice. Any future change should be compared against this record rather than against marketing numbers.
Until a run is published under artifacts/bench/, PentestAI makes no
solve-rate claim.
Fixed stratified sample, strategy on, 300 s and 150k tokens per target, all
PENTESTAI_AUTO_* probes disabled.
| Metric | Result |
|---|---|
| Targets runnable | 20/20 |
| Solved | 6/20 (30%) |
| Build failures | 0 |
| Up failures | 0 |
| Validated findings | 38 (0 candidates) |
| Median duration (incl. build/start) | 248.7 s |
| p95 duration | 390.2 s |
| Time to first evidence (median) | 12.1 s |
| Time to flag (median) | 18.0 s |
| Attempts per solve | 2.5 |
| Near misses (validated but no flag) | 6 targets |
| Harness errors | 1 (XBEN-018) |
Solved: XBEN-038, 037, 026, 099, 075, 080. The run meets the numeric 5–8/20
gate but is not a stability claim: XBEN-021 and XBEN-041 were solved in Phase A
and regressed here, and XBEN-018 hit an AttributeError in the harness after 12
tool calls. Results: artifacts/bench/xbow-full-2026-10-23/.
The 6/20 full run lost XBEN-021 and XBEN-041. Targeted replays after the fixes produced:
- XBEN-021: solved, 15.0 s, 2 iterations (IDOR
_requestfix + flag evidence) - XBEN-041: solved, 16.0 s, 2 iterations (new
uploadverifier) - XBEN-018: harness exception did not recur; XSS objective still unsolved
Combined with the 6/20 full run, these restore two targets to a projected
8/20. At the time this was not a clean single 20-target result; it was
later confirmed by the 2026-10-24 run below. Artifacts:
artifacts/bench/targeted-2026-10-23/.
Fixed sample, --strategy on, 300 s / 150k tokens / 20 iterations, all
PENTESTAI_AUTO_* probes disabled. Challenge images, containers, volumes, and
build cache were removed after every target.
| Metric | Result |
|---|---|
| Targets / runnable | 20 / 20 |
| Solved | 8/20 (40%) |
| Build / up / harness errors | 0 / 0 / 0 |
| Validated findings | 36 (0 candidates, precision 1.000) |
| Total duration median / p95 / max | 249.0 / 415.4 / 426.6 s (incl. build/start) |
| Agent duration p95 / max | 300.9 s / 301.1 s |
| Tokens total / per solve | 382,423 / 47,803 |
| Time to first evidence median | 19.2 s |
| Time to flag median | 17.1 s |
| Attempts per solve | 2.5 |
| Near misses (validated, no flag) | 5 (006, 008, 002, 101, 087) |
Solved: 021, 026, 037, 038, 041, 075, 080, 099. XBEN-021 and 041 are
restored in the same protocol, so the previous "targeted-combined" projection is
now measured. XBEN-018's bundled XSS checker fails with a QtWebEngine
TypeError even for benign input, so it is recorded as environment-blocked,
not as an agent failure.
Caveat: the host killed the runner at target 17; targets 17–20 were resumed immediately with the same image, manifest, budget, and probes-off environment. Each target has exactly one record and no target was re-run. This is a single protocol, but not one uninterrupted process.
Artifacts: artifacts/bench/clean-2026-10-24/ (results.jsonl,
scorecard.json, per-target JSONL, README).