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>
265 行
8.6 KiB
Python
265 行
8.6 KiB
Python
import json
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import sys
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from types import SimpleNamespace as NS
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import pytest
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from openai_codex import Sandbox
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from unified_agent.backends.codex import (
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CodexBackend,
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_TurnState,
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build_config_overrides,
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build_thread_kwargs,
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build_turn_kwargs,
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normalize_notification,
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)
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from unified_agent.events import (
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AssistantText,
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CommandRun,
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FileChanged,
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RawEvent,
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Reasoning,
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SessionStarted,
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TextDelta,
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ToolCall,
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ToolResult,
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TurnCompleted,
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)
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from unified_agent.types import RunOptions, SandboxPolicy, ToolServerSpec
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def opts(tmp_path, **kw) -> RunOptions:
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return RunOptions(workspace=tmp_path, **kw)
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TOOL_SERVER = ToolServerSpec(
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server_name="unified",
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command=[sys.executable, "-m", "unified_agent.tool_server", "tests.fixture_registry:REG"],
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env={"PYTHONPATH": "/re po"}, # space exercises TOML string quoting
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)
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# --- config / thread option mapping ----------------------------------------
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def test_config_overrides_define_the_mcp_server_inline():
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overrides = build_config_overrides(TOOL_SERVER)
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joined = "\n".join(overrides)
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assert f"mcp_servers.unified.command={json.dumps(sys.executable)}" in overrides
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args_line = next(o for o in overrides if ".args=" in o)
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assert json.loads(args_line.split("=", 1)[1]) == [
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"-m",
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"unified_agent.tool_server",
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"tests.fixture_registry:REG",
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]
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env_line = next(o for o in overrides if ".env=" in o)
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assert env_line == 'mcp_servers.unified.env={ "PYTHONPATH" = "/re po" }'
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assert "mcp_servers.unified.startup_timeout_sec=30" in joined
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assert "mcp_servers.unified.tool_timeout_sec=120" in joined
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assert "mcp_servers.unified.required=true" in joined
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assert 'mcp_servers.unified.default_tools_approval_mode="auto"' in joined
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@pytest.mark.parametrize(
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"policy,expected",
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[
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(SandboxPolicy.READ_ONLY, Sandbox.read_only),
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(SandboxPolicy.WORKSPACE_WRITE, Sandbox.workspace_write),
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(SandboxPolicy.FULL_ACCESS, Sandbox.full_access),
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],
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)
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def test_thread_kwargs_sandbox_mapping(tmp_path, policy, expected):
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kw = build_thread_kwargs(opts(tmp_path, sandbox=policy))
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assert kw["sandbox"] is expected
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assert kw["cwd"] == str(tmp_path)
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def test_thread_kwargs_passthroughs(tmp_path):
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kw = build_thread_kwargs(opts(tmp_path, model="gpt-5.5", instructions="be terse"))
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assert kw["model"] == "gpt-5.5"
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assert kw["developer_instructions"] == "be terse"
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kw2 = build_thread_kwargs(opts(tmp_path))
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assert "model" not in kw2 and "developer_instructions" not in kw2
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def test_turn_kwargs_effort_mapping(tmp_path):
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from openai_codex.generated.v2_all import ReasoningEffort
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kw = build_turn_kwargs(opts(tmp_path, effort="xhigh"))
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assert kw["effort"] is ReasoningEffort.xhigh
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assert build_turn_kwargs(opts(tmp_path)) == {"output_schema": None}
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def test_turn_kwargs_invalid_effort(tmp_path):
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from unified_agent.types import AgentRunError
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with pytest.raises(AgentRunError, match="effort"):
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build_turn_kwargs(opts(tmp_path, effort="max")) # claude-only level
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# --- notification normalization ---------------------------------------------
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def fresh_state(tmp_path, **kw) -> _TurnState:
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return _TurnState(opts=opts(tmp_path, **kw))
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def norm(method, payload, state):
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return list(normalize_notification(method, payload, state))
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def test_thread_started_announces_session_once(tmp_path):
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state = fresh_state(tmp_path)
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events = norm("thread/started", NS(thread=NS(id="thr-1")), state)
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assert events == [SessionStarted(session_id="thr-1")]
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assert norm("thread/started", NS(thread=NS(id="thr-1")), state) == []
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def test_agent_message_item(tmp_path):
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state = fresh_state(tmp_path)
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item = NS(root=NS(type="agentMessage", text="hello there", id="i1"))
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events = norm("item/completed", NS(item=item), state)
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assert events == [AssistantText(text="hello there")]
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assert state.last_agent_text == "hello there"
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def test_reasoning_item_prefers_summary(tmp_path):
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state = fresh_state(tmp_path)
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item = NS(root=NS(type="reasoning", summary=["thought hard"], content=[], id="i2"))
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assert norm("item/completed", NS(item=item), state) == [Reasoning(text="thought hard")]
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def test_command_execution_item(tmp_path):
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state = fresh_state(tmp_path)
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item = NS(
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root=NS(
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type="commandExecution",
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command="ls -la",
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exit_code=0,
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aggregated_output="total 0",
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status="completed",
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id="i3",
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)
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)
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assert norm("item/completed", NS(item=item), state) == [
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CommandRun(command="ls -la", exit_code=0, output="total 0")
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]
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def test_mcp_tool_call_item(tmp_path):
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state = fresh_state(tmp_path)
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item = NS(
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root=NS(
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type="mcpToolCall",
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server="unified",
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tool="add_numbers",
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arguments='{"a": 2, "b": 3}',
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result=NS(content=[NS(type="text", text="2 + 3 = 5")], structured_content=None),
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error=None,
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status="completed",
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id="i4",
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)
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)
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call, result = norm("item/completed", NS(item=item), state)
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assert call == ToolCall(name="mcp__unified__add_numbers", input={"a": 2, "b": 3}, call_id="i4")
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assert isinstance(result, ToolResult)
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assert "5" in result.output and result.is_error is False
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def test_failed_mcp_tool_call_marks_error(tmp_path):
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state = fresh_state(tmp_path)
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item = NS(
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root=NS(
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type="mcpToolCall",
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server="unified",
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tool="boom",
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arguments=None,
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result=None,
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error=NS(message="exploded"),
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status="failed",
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id="i5",
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)
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)
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_call, result = norm("item/completed", NS(item=item), state)
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assert result.is_error is True
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assert "exploded" in result.output
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def test_file_change_item(tmp_path):
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state = fresh_state(tmp_path)
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item = NS(
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root=NS(
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type="fileChange",
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status="completed",
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changes=[NS(path="a.txt", kind="add"), NS(path="b.txt", kind="update")],
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id="i6",
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)
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)
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assert norm("item/completed", NS(item=item), state) == [
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FileChanged(path="a.txt", kind="add"),
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FileChanged(path="b.txt", kind="update"),
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]
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def test_agent_message_delta_gated_by_stream_text(tmp_path):
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on = fresh_state(tmp_path, stream_text=True)
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off = fresh_state(tmp_path, stream_text=False)
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assert norm("item/agentMessage/delta", NS(delta="he"), on) == [TextDelta(text="he")]
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assert norm("item/agentMessage/delta", NS(delta="he"), off) == []
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def test_token_usage_tracked_then_reported_on_turn_completed(tmp_path):
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state = fresh_state(tmp_path)
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usage = NS(
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total=NS(
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input_tokens=100,
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cached_input_tokens=40,
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output_tokens=9,
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reasoning_output_tokens=3,
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total_tokens=112,
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),
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last=None,
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model_context_window=None,
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)
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assert norm("thread/tokenUsage/updated", NS(token_usage=usage, turn_id="t"), state) == []
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state.thread_id = "thr-1"
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state.last_agent_text = "final answer"
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turn = NS(id="t", status="completed", error=None)
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[event] = norm("turn/completed", NS(turn=turn, thread_id="thr-1"), state)
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assert isinstance(event, TurnCompleted)
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assert event.success is True
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assert event.final_text == "final answer"
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assert event.usage.input_tokens == 100
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assert event.usage.cached_input_tokens == 40
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assert event.usage.reasoning_output_tokens == 3
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assert event.session_id == "thr-1"
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assert event.cost_usd is None
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def test_turn_failed_with_error_notification(tmp_path):
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state = fresh_state(tmp_path)
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norm("error", NS(error=NS(message="rate limited"), will_retry=False), state)
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turn = NS(id="t", status=NS(value="failed"), error=NS(message="turn died"))
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[event] = norm("turn/completed", NS(turn=turn), state)
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assert event.success is False
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assert "turn died" in event.error and "rate limited" in event.error
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def test_structured_output_parsed_from_final_text(tmp_path):
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state = fresh_state(tmp_path, output_schema={"type": "object"})
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state.last_agent_text = '{"n": 42}'
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turn = NS(id="t", status="completed", error=None)
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[event] = norm("turn/completed", NS(turn=turn), state)
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assert event.structured_output == {"n": 42}
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def test_unknown_notification_becomes_raw_event(tmp_path):
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state = fresh_state(tmp_path)
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[event] = norm("guardianWarning", NS(anything=1), state)
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assert isinstance(event, RawEvent)
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assert event.kind == "guardianWarning"
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def test_backend_name():
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assert CodexBackend().name == "codex"
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