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>
292 行
11 KiB
Python
292 行
11 KiB
Python
"""Codex backend via the official openai-codex SDK (drives `codex app-server`).
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The shared MCP tool server is injected per-client through `-c`-style config
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overrides (`mcp_servers.<name>.*`), the documented mechanism mirroring the CLI.
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Codex does NOT inherit the parent environment into MCP server processes, so the
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ToolServerSpec env (PYTHONPATH etc.) is passed explicitly as a TOML inline table.
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Usage note: turn token usage arrives via `thread/tokenUsage/updated`
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notifications (the `turn/completed` payload carries no usage), so the
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normalizer tracks the latest usage statefully.
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"""
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from __future__ import annotations
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import contextlib
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import inspect
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import json
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from collections.abc import AsyncIterator, Iterator
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from typing import Any
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from openai_codex import AsyncCodex, CodexConfig, Sandbox
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from ..events import (
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AgentEvent,
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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 ..types import (
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AgentAuthError,
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AgentRunError,
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RunOptions,
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SandboxPolicy,
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ToolServerSpec,
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UnifiedUsage,
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)
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SANDBOX_MAP = {
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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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def _toml_string(value: str) -> str:
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# A JSON string is a valid TOML basic string (same escaping rules for
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# quotes/backslashes/control chars).
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return json.dumps(value)
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def build_config_overrides(spec: ToolServerSpec) -> tuple[str, ...]:
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"""`-c key=value` style overrides defining the shared MCP server."""
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key = f"mcp_servers.{spec.server_name}"
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env_table = (
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"{ " + ", ".join(f'"{k}" = {_toml_string(v)}' for k, v in sorted(spec.env.items())) + " }"
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)
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return (
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f"{key}.command={_toml_string(spec.command[0])}",
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f"{key}.args={json.dumps(spec.command[1:])}", # JSON array == TOML array of strings
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f"{key}.env={env_table}",
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f"{key}.startup_timeout_sec=30",
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f"{key}.tool_timeout_sec=120",
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f"{key}.required=true",
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f'{key}.default_tools_approval_mode="auto"',
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)
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def build_turn_kwargs(opts: RunOptions) -> dict[str, Any]:
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"""Per-turn kwargs for Thread.turn()/run(): output schema + reasoning effort."""
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kwargs: dict[str, Any] = {"output_schema": opts.output_schema}
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if opts.effort:
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from openai_codex.generated.v2_all import ReasoningEffort
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try:
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kwargs["effort"] = ReasoningEffort(opts.effort)
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except ValueError as e:
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valid = ", ".join(m.value for m in ReasoningEffort)
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raise AgentRunError(f"invalid effort {opts.effort!r} for codex (valid: {valid})") from e
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return kwargs
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def build_thread_kwargs(opts: RunOptions) -> dict[str, Any]:
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kwargs: dict[str, Any] = {
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"cwd": str(opts.workspace),
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"sandbox": SANDBOX_MAP[opts.sandbox],
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}
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if opts.model:
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kwargs["model"] = opts.model
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if opts.instructions:
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kwargs["developer_instructions"] = opts.instructions
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return kwargs
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class _TurnState:
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"""Mutable accumulator across one turn's notification stream."""
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def __init__(self, opts: RunOptions):
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self.opts = opts
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self.thread_id: str | None = None
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self.session_announced = False
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self.last_agent_text: str | None = None
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self.usage: Any | None = None # latest ThreadTokenUsage
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self.errors: list[str] = []
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def _status_str(status: Any) -> str:
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return str(getattr(status, "value", status))
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def _stringify_mcp_result(result: Any) -> str:
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if result is None:
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return ""
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structured = getattr(result, "structured_content", None)
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if structured:
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try:
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return json.dumps(structured)
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except TypeError:
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return str(structured)
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parts = []
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for block in getattr(result, "content", None) or []:
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text = getattr(block, "text", None)
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parts.append(text if text is not None else str(block))
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return "\n".join(parts)
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def _normalize_item(root: Any, state: _TurnState) -> Iterator[AgentEvent]:
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kind = getattr(root, "type", None)
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if kind == "agentMessage":
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text = getattr(root, "text", "") or ""
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state.last_agent_text = text
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yield AssistantText(text=text)
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elif kind == "reasoning":
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chunks = list(getattr(root, "summary", None) or []) or list(
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getattr(root, "content", None) or []
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)
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text = "\n".join(str(c) for c in chunks)
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if text:
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yield Reasoning(text=text)
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elif kind == "commandExecution":
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yield CommandRun(
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command=getattr(root, "command", "") or "",
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exit_code=getattr(root, "exit_code", None),
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output=getattr(root, "aggregated_output", "") or "",
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)
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elif kind == "mcpToolCall":
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server = getattr(root, "server", "") or ""
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tool = getattr(root, "tool", "") or ""
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arguments = getattr(root, "arguments", None)
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if isinstance(arguments, str):
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with contextlib.suppress(json.JSONDecodeError, ValueError):
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arguments = json.loads(arguments)
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call_id = getattr(root, "id", None)
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yield ToolCall(name=f"mcp__{server}__{tool}", input=arguments, call_id=call_id)
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error = getattr(root, "error", None)
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failed = _status_str(getattr(root, "status", "")) != "completed" or error is not None
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output = (
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getattr(error, "message", None) or str(error)
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if error is not None
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else _stringify_mcp_result(getattr(root, "result", None))
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)
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yield ToolResult(call_id=call_id, output=output or "", is_error=failed)
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elif kind == "fileChange":
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for change in getattr(root, "changes", None) or []:
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yield FileChanged(
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path=str(getattr(change, "path", change)),
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kind=str(getattr(change, "kind", "update")),
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)
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else:
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yield RawEvent(backend="codex", kind=f"item:{kind}", data=root)
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def _usage_from_state(state: _TurnState) -> UnifiedUsage:
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total = getattr(state.usage, "total", None)
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if total is None:
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return UnifiedUsage()
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return UnifiedUsage(
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input_tokens=getattr(total, "input_tokens", 0) or 0,
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cached_input_tokens=getattr(total, "cached_input_tokens", 0) or 0,
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output_tokens=getattr(total, "output_tokens", 0) or 0,
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reasoning_output_tokens=getattr(total, "reasoning_output_tokens", 0) or 0,
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)
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def normalize_notification(method: str, payload: Any, state: _TurnState) -> Iterator[AgentEvent]:
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if method == "thread/started":
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thread = getattr(payload, "thread", None)
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thread_id = getattr(thread, "id", None)
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if thread_id:
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state.thread_id = thread_id
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if not state.session_announced and state.thread_id:
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state.session_announced = True
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yield SessionStarted(session_id=state.thread_id)
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elif method == "item/completed":
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item = getattr(payload, "item", None)
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root = getattr(item, "root", item)
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if root is not None:
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yield from _normalize_item(root, state)
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elif method == "item/agentMessage/delta":
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if state.opts.stream_text:
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yield TextDelta(text=getattr(payload, "delta", "") or "")
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elif method == "thread/tokenUsage/updated":
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state.usage = getattr(payload, "token_usage", None)
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elif method == "error":
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error = getattr(payload, "error", None)
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message = getattr(error, "message", None) or str(error)
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state.errors.append(message)
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yield RawEvent(backend="codex", kind="error", data=message)
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elif method == "turn/completed":
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turn = getattr(payload, "turn", None)
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status = _status_str(getattr(turn, "status", ""))
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success = status == "completed"
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turn_error = getattr(turn, "error", None)
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error_parts = []
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if turn_error is not None:
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error_parts.append(getattr(turn_error, "message", None) or str(turn_error))
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error_parts.extend(state.errors)
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structured = None
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if state.opts.output_schema and state.last_agent_text:
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try:
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structured = json.loads(state.last_agent_text)
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except (json.JSONDecodeError, ValueError):
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structured = None
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duration = getattr(turn, "duration_ms", None)
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yield TurnCompleted(
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success=success,
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final_text=state.last_agent_text,
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usage=_usage_from_state(state),
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cost_usd=None, # Codex reports tokens only
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session_id=state.thread_id,
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duration_ms=duration,
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structured_output=structured,
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stop_reason=status,
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error=None if success else ("; ".join(p for p in error_parts if p) or status),
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)
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elif method in ("turn/started", "item/started", "item/updated"):
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pass # uninteresting transitions; item/completed carries the substance
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else:
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yield RawEvent(backend="codex", kind=method, data=payload)
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def _looks_like_auth_error(exc: Exception) -> bool:
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text = str(exc).lower()
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return any(s in text for s in ("auth", "login", "unauthorized", "api key", "logged out"))
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class CodexBackend:
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name = "codex"
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async def stream(self, prompt: str, opts: RunOptions) -> AsyncIterator[AgentEvent]:
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overrides = build_config_overrides(opts.tool_server) if opts.tool_server else ()
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config = CodexConfig(config_overrides=tuple(overrides), cwd=str(opts.workspace))
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state = _TurnState(opts)
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try:
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async with AsyncCodex(config) as codex:
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if opts.resume:
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thread = await codex.thread_resume(opts.resume, **build_thread_kwargs(opts))
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else:
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thread = await codex.thread_start(**build_thread_kwargs(opts))
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state.thread_id = getattr(thread, "id", None)
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if state.thread_id and not state.session_announced:
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state.session_announced = True
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yield SessionStarted(session_id=state.thread_id)
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handle = thread.turn(prompt, **build_turn_kwargs(opts))
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if inspect.isawaitable(handle):
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handle = await handle
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async for note in handle.stream():
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method = getattr(note, "method", "")
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payload = getattr(note, "payload", None)
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for event in normalize_notification(method, payload, state):
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yield event
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except (AgentRunError, AgentAuthError):
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raise
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except Exception as e: # JSON-RPC / transport errors from the SDK
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if _looks_like_auth_error(e):
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raise AgentAuthError(f"Codex is not authenticated (run `codex login`): {e}") from e
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raise AgentRunError(f"Codex run failed: {type(e).__name__}: {e}") from e
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