greydgl--pentestgpt
ab5fbb4d90
* first refactor
* feat: dockerized tool with persistent Claude+Codex login + multi-model benchmark
Run the autonomous CTF/pentest tool in Docker with a one-time, persistent login for
BOTH Claude Code and Codex, and add a multi-model benchmark harness.
Backend (multi-model):
- Add `--backend {claude,codex}` to the CTF pipeline. CodexBackend (pentestgpt/core/
backend.py) wraps unified_agent's Codex backend and translates its events into
AgentMessages, so the same pipeline runs on Claude (opus/sonnet) or Codex
(gpt-5.5/gpt-5.4-mini). Wired through config.backend, pipeline stage construction,
and the CLI (+ PENTESTGPT_CODEX_EFFORT; greppable [CODEX_USAGE] under PENTESTGPT_BENCH=1).
Docker tool (tool-only image; the benchmark stays OUTSIDE the image):
- Extend Dockerfile: Codex CLI (@openai/codex) + openai_codex SDK + unified_agent/
pentestgpt_agent/pentestgpt_legacy packages + gobuster/dirb + socat. Add .dockerignore
(keeps creds/benchmark/workspace out of the build context).
- Persistent dual login (the hard part) — asymmetric by token model:
* Claude: `setup-token` -> token stored in the pentestgpt-claude volume; entrypoint
exports CLAUDE_CODE_OAUTH_TOKEN (setup-token does not write .credentials.json; macOS
host creds live in the Keychain and can't be copied).
* Codex: the container does its OWN `codex login` (NOT seeding -- ChatGPT refresh tokens
are single-use, so a shared/copied login 401s on first refresh). The 127.0.0.1:1455
OAuth callback is forwarded into the container via a socat hop (-p 1455:8455).
* scripts/docker-login.sh is idempotent: checks logins live, logs in only the missing one(s).
- docker-compose codex-config volume (+ pinned names); entrypoint token-export + non-blocking
preflight; scripts/docker-auth-status.sh; Make targets (docker-build/login/auth-status/
run/shell/down/nuke).
- Verified end-to-end: one `make docker-login` -> a fresh container reports claude+codex
logged in with live round-trips; the CTF pipeline (Codex) captured a flag against an
isolated fixture and the pentest pipeline ran cleanly; persists across recreation, no re-login.
Benchmark (multi-model, host-side):
- benchmark/pilot/ harness (run_pilot.py + report.py): builds each xbow challenge, discovers
the loopback port, runs the pipeline across the 4 model combos, judges by the baked
FLAG{sha256(UPPER-dir)}, and renders REPORT.md (infra failures excluded from solve rates).
Includes the partial pilot's results (results.jsonl + REPORT.md).
Docs: docs/docker-dev-plan.md (full plan + implementation status); CLAUDE.md and README
docker quickstart; benchmark/pilot/README.md; design-doc roadmap (docs/redesign).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix: fail controller on backend error messages
* fix: allow listing sessions without target
* docs: add docker xbow benchmark report
* fix: infer concrete backend constructor type
* docs: refresh docker benchmark documentation
* feat(benchmark): add pure single-agent baseline + pipeline comparison
Add a "pure single agent" benchmark variant -- one bare `claude -p` /
`codex exec` call per target (no pipeline) -- to quantify what the 3-stage
PentestGPT pipeline buys over an un-orchestrated agent on the xbow targets.
- pentestgpt/prompts/stages.py: ctf_single_agent_{system,task}_prompt -- the
pipeline's shared fragments collapsed into ONE turn, so prompt content is
held constant and the only variable is the multi-stage decomposition.
- benchmark/pilot/run_docker_bench.py: docker-network runner
(--variant single|pipeline). Brings the target up, discovers the container's
internal IP+network (skips DB side-cars/ports), docker-runs the tool image on
that network, and scores the ground-truth flag against the agent's *assistant
text* only (parity with the pipeline's raw streaming). Reads stdout in chunks
to handle >64KB JSON lines. Resumable; --dry-run supported.
- benchmark/pilot/report_comparison.py -> DOCKER_COMPARISON.md: head-to-head
pipeline-vs-single per model on the common non-infra set.
- tests/unit/test_single_agent_prompt.py: prompt-builder coverage.
- docs: README, CLAUDE.md, benchmark README, DOCKER_REPORT updated.
Recorded result (10 medium/hard targets x 4 models, container-to-container,
same baseline image digest 0c4c0f3e..., commit dca0019 image):
Model Pipeline Single
Claude Opus 5/10 7/10 (single +2)
Claude Sonnet 6/10 4/10 (pipeline +2)
Codex gpt-5.5 7/10 7/10 (tie)
Codex gpt-5.4-mini 3/10 4/10 (single +1)
TOTAL 21/40 22/40
Single agent matches the pipeline on solve rate (55% vs 52%) while using
~40% fewer Codex tokens (13.0M vs 21.8M) and solving faster. The pipeline
only clearly helps Claude Sonnet (which times out solo); Opus is better solo.
Full per-challenge grid in DOCKER_COMPARISON.md; raw records in
docker_single_results.jsonl.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* feat(benchmark): add pentestgpt_agent docker harness
* bench: refresh pentestgpt_agent smoke result
* fix(benchmark): make repeat rows variant-aware
* fix(agent): fall back for semantic executor labels
* fix(agent): tolerate executor prose evidence
* fix(benchmark): score accepted framework findings
* bench: append partial framework repeat results
* bench: complete framework repeat sweep
* bench: expose framework executor concurrency
* bench: add extended parallel framework sweep
* checkpoint: preserve working agent and benchmark state
* feat: harden durable agent loop and xbow qualification
* fix: reserve an exploit result turn
* docs: record clean xbow qualification
* build: consume unified-agent from the git wrapper repo
Repoint pentestgpt_agent_new's unified-agent dependency from the local
editable path (../../UnifiedAgentPoC, now renamed and gone) to the pinned
git source PentestGPT-Project/UnifedAgentWrapper@d05d21f. Regenerate uv.lock
and update test_dependency.py to assert the external package is installed
from that VCS URL (not the repo-root vendored copy) at version 0.2.0.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* refactor: make pentestgpt_agent_new the sole framework
Remove the retired ledger-based pentestgpt_agent package (instructor/executor/
judge) and its orphaned unit + smoke tests. The nested pentestgpt_agent_new
project (Supervisor/Executor over a durable SQLite loop, consuming unified-agent
from the git wrapper) is now the single maintained framework.
Repoint the top-level tooling to it:
- pyproject: drop the pentestgpt-agent console script and pentestgpt_agent from
the wheel packages.
- Makefile: lint/format target parent code only; typecheck/check/ci now run the
nested framework's own gate (ruff, format, mypy, pytest) via test-agent-new /
check-agent-new, so `make check` finally covers it; `make run` delegates to the
pentestgpt-agent-new CLI.
- Dockerfile: stop copying the removed package (kept the build working); note the
framework is not baked into the image yet.
- docker container-health test: import the substrate packages that actually ship.
- CLAUDE.md / AGENT.md: describe the new framework, the git-sourced wrapper, and
the deprioritized benchmark/Docker rewire.
The XBOW `--variant framework` path and docker-bench Makefile targets still point
at the old in-image framework and are left as a pending rewire (benchmarks
deprioritized); the naive `--variant single` path is unaffected.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* refactor: rename pentestgpt_agent_new -> pentestgpt_agent
The framework reclaims the clean name now that the old ledger-based package is
gone. Rename the nested project folder, its src package, the distribution
(pentestgpt-agent-new -> pentestgpt-agent) and CLI, and every import/reference in
the package, the umbrella Makefile, the Dockerfile, the docker health test, and
CLAUDE.md / AGENT.md. Regenerate uv.lock. The audit CLI stays pentestgpt-agent-audit;
the git-sourced unified-agent dependency is unchanged. `make check` is green
(108 nested tests). The two historical *_REPORT.md files keep the old name as
dated records.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* chore: extract benchmark harness to sibling xbow-benchmark repo
Move PentestGPT/benchmark/ out to ../xbow-benchmark (its own repo) to keep this
project clean. The harness was decoupled from the framework code (it scores
container output, never imports pentestgpt_agent/unified_agent), so only
operational ties remain and they now live in the sibling repo.
- Remove benchmark/ and the 4 harness unit tests (relocated + repointed there).
- Strip the docker-bench-*/bench-* targets and their config vars from the
Makefile; keep the tool-image lifecycle (docker-build/login/run/...) and add a
help pointer to `make -C ../xbow-benchmark help`.
The sibling repo mounts this checkout read-only (--source-root ../PentestGPT) and
runs the pentestgpt:latest image built here.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* feat: harden autonomous framework and runtime integration
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
211 行
7.0 KiB
Python
211 行
7.0 KiB
Python
import json
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from collections.abc import AsyncIterator
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from pathlib import Path
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import pytest
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from unified_agent import AgentEvent, RunOptions, SandboxPolicy, TurnCompleted, UnifiedAgent
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from pentestgpt_agent.agents import EXECUTOR_INSTRUCTIONS, Executor
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from pentestgpt_agent.memory import (
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AttemptRecord,
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AttemptStatus,
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ObservationRecord,
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RunSnapshot,
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RunStatus,
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TaskLease,
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)
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from pentestgpt_agent.plan import TaskKind, TaskRecord, TaskStatus
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from pentestgpt_agent.trace import EpisodeRunner, TraceStore
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class CapturingExecutorBackend:
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name = "capturing"
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def __init__(self) -> None:
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self.prompt: str | None = None
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self.max_turns: int | None = None
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async def stream(self, prompt: str, opts: RunOptions) -> AsyncIterator[AgentEvent]:
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self.prompt = prompt
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self.max_turns = opts.max_turns
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yield TurnCompleted(
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success=True,
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structured_output={
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"task_id": "active-task",
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"outcome": "progress",
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"summary": "A bounded next action remains.",
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"evidence_excerpt": None,
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},
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)
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def _active_snapshot(kind: TaskKind) -> tuple[RunSnapshot, TaskLease]:
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task = TaskRecord(
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id="active-task",
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kind=kind,
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target="http://target.test/input",
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objective="Assess only the named surface.",
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done_when="The named hypothesis is resolved.",
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basis_ids=(),
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depends_on=(),
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status=TaskStatus.ACTIVE,
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created_revision=1,
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)
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snapshot = RunSnapshot(
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run_id="run-1",
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goal="Capture the flag.",
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allowed_targets=("http://target.test",),
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status=RunStatus.RUNNING,
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revision=2,
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max_attempts_per_task=2,
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tasks=(task,),
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)
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return snapshot, TaskLease(
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run_id="run-1",
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task_id=task.id,
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attempt_id="attempt-1",
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revision=2,
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)
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@pytest.mark.asyncio
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async def test_executor_caps_test_episode_and_exposes_its_turn_budget(tmp_path: Path) -> None:
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backend = CapturingExecutorBackend()
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executor = Executor(
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EpisodeRunner(
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UnifiedAgent(
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backend,
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workspace=tmp_path / "workspace",
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sandbox=SandboxPolicy.WORKSPACE_WRITE,
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instructions=EXECUTOR_INSTRUCTIONS,
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),
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TraceStore(tmp_path / "runs"),
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),
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max_turns=12,
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)
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snapshot, lease = _active_snapshot(TaskKind.TEST)
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await executor.execute(snapshot, lease, episode_id="executor-1")
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assert backend.max_turns == 6
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assert backend.prompt is not None
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assert '"turn_budget": 5' in backend.prompt
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@pytest.mark.asyncio
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async def test_executor_retry_receives_bounded_non_evidentiary_diagnostics_and_own_evidence(
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tmp_path: Path,
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) -> None:
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backend = CapturingExecutorBackend()
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executor = Executor(
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EpisodeRunner(
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UnifiedAgent(
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backend,
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workspace=tmp_path / "workspace",
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sandbox=SandboxPolicy.WORKSPACE_WRITE,
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instructions=EXECUTOR_INSTRUCTIONS,
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),
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TraceStore(tmp_path / "runs"),
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),
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max_turns=12,
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)
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base, lease = _active_snapshot(TaskKind.TEST)
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snapshot = RunSnapshot(
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**{
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**base.__dict__,
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"observations": (
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ObservationRecord(
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id="obs-progress",
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task_id="active-task",
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attempt_id="attempt-previous",
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statement="uid=0(root)",
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trace_episode_id="executor-previous",
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evidence_sequences=(7,),
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created_revision=2,
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),
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),
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"attempts": (
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AttemptRecord(
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id="attempt-previous",
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task_id="active-task",
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status=AttemptStatus.ERROR,
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started_revision=1,
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finished_revision=2,
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trace_episode_id="executor-previous",
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summary="Provider rejected the malformed result.",
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failure_kind="validation",
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failure_message="evidence was not an exact quote",
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),
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),
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}
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)
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await executor.execute(snapshot, lease, episode_id="executor-retry")
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assert backend.prompt is not None
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envelope = json.loads(backend.prompt.split("\n\n", 1)[1])
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assert envelope["prior_task_evidence"] == [{"id": "obs-progress", "evidence": "uid=0(root)"}]
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assert envelope["retry_diagnostic"] == {
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"status": "error",
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"failure_kind": "validation",
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"failure_message": "evidence was not an exact quote",
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}
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assert "Provider rejected the malformed result." not in backend.prompt
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def test_executor_instructions_protect_the_result_turn_and_test_boundary() -> None:
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assert "Immediately return StructuredOutput when done_when is met" in EXECUTOR_INSTRUCTIONS
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assert "runtime reserves one additional transport turn" in EXECUTOR_INSTRUCTIONS
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assert "quote the complete contiguous result block" in EXECUTOR_INSTRUCTIONS
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assert "TEST never pursues or retrieves the run goal" in EXECUTOR_INSTRUCTIONS
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assert "only one command token survives and '$' is filtered" in EXECUTOR_INSTRUCTIONS
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assert "prefer shell input redirection" in EXECUTOR_INSTRUCTIONS
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assert "Tool-call timeout metadata is not an operating-system bound" in EXECUTOR_INSTRUCTIONS
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assert "Prefer userspace protocol clients over kernel filesystem mounts" in (
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EXECUTOR_INSTRUCTIONS
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)
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assert "Never issue an unprivileged kernel filesystem mount" in EXECUTOR_INSTRUCTIONS
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assert "sudo -n -l" in EXECUTOR_INSTRUCTIONS
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@pytest.mark.parametrize(
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("kind", "requested", "expected_task_turns", "expected_provider_turns"),
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(
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(TaskKind.DISCOVER, 12, 5, 6),
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(TaskKind.ENUMERATE, 12, 6, 7),
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(TaskKind.TEST, 12, 5, 6),
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(TaskKind.EXPLOIT, 12, 9, 10),
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(TaskKind.VERIFY, 12, 4, 5),
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(TaskKind.RECOVER, 12, 6, 7),
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(TaskKind.EXPLOIT, 3, 2, 3),
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),
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)
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@pytest.mark.asyncio
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async def test_executor_enforces_the_smaller_requested_or_per_kind_budget(
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tmp_path: Path,
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kind: TaskKind,
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requested: int,
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expected_task_turns: int,
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expected_provider_turns: int,
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) -> None:
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backend = CapturingExecutorBackend()
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executor = Executor(
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EpisodeRunner(
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UnifiedAgent(
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backend,
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workspace=tmp_path / "workspace",
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sandbox=SandboxPolicy.WORKSPACE_WRITE,
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instructions=EXECUTOR_INSTRUCTIONS,
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),
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TraceStore(tmp_path / "runs"),
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),
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max_turns=requested,
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)
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snapshot, lease = _active_snapshot(kind)
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await executor.execute(snapshot, lease, episode_id=f"executor-{kind.value}")
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assert backend.max_turns == expected_provider_turns
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assert backend.prompt is not None
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assert f'"turn_budget": {expected_task_turns}' in backend.prompt
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