docs/xbow-reference-only
10 次代码提交
| 作者 | SHA1 | 备注 | 提交日期 | |
|---|---|---|---|---|
 Gelei Deng
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ab5fbb4d90 |
feat: ship the durable multi-model autonomous PentestGPT runtime (#493)
* 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
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Gelei Deng
|
b9869307d0 |
Legacy multi llm base (#470)
* fix: 🐛 minor typo and build process * feat: 🎸 [WIP] Pentest mode * feat: 🎸 code abstraction * feat: modernize legacy PentestGPT with native multi-LLM support (#469) Rebuild the classic USENIX-2024 interactive PentestGPT (reasoning / generation / parsing sessions + Pentesting Task Tree + REPL) as a standalone `pentestgpt_legacy` package on a native per-provider LLM layer that supports the latest 2026 models. - llm/: BaseProvider + OpenAI-compatible / Anthropic / Gemini connectors, a web-verified model registry (OpenAI, Anthropic, Gemini, DeepSeek, xAI, Qwen, Moonshot, local Ollama), a factory, and an LLMClient bridging async providers to the core's synchronous send_new_message/send_message session API. - CLI `pentestgpt-legacy`: --list-models and --smoke-test (live per-model round-trip matrix), plus --reasoning-model / --parsing-model / --base-url. - Tests: 25 unit tests (mocked, no network). Live smoke test verified 22/22 models with a configured key respond. Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> * fix(backend): address review on ClaudeCodeBackend subprocess handling - _build_env: pop ANTHROPIC_API_KEY instead of setting it to "", so an empty value can't shadow the CLI's own auth fallback (e.g. subscription login). - _kill_process: reap the force-killed process with os.waitpid(.., WNOHANG) instead of calling the proc.wait() coroutine without awaiting it (removes the "coroutine was never awaited" warning). - query/_drain_stderr: drain subprocess stderr in a background task so its pipe buffer can't fill and deadlock the child. Also reformats backend.py, fixing the failing Lint (ruff format) check. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * fix(docker-test): assert uv instead of Poetry in container health check The project migrated from Poetry to uv (the Dockerfile installs uv to /home/pentester/.local/bin, which is on PATH), so test_poetry_installed failed with exit 127. Replace it with test_uv_installed checking `uv --version`. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> |
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Gelei Deng
|
e238d701f2 |
feat: 🎸 version 1.0 agentic workflow (#325)
* feat: 🎸 version 1.0 agentic workflow
Major rewrite of PentestGPT to use an agentic pipeline architecture:
Core Changes: - New event-driven architecture with EventBus for
TUI-agent decoupling - Implemented AgentController with 5-state
lifecycle (IDLE->RUNNING->PAUSED->COMPLETED->ERROR) - Added AgentBackend
interface with ClaudeCodeBackend implementation - Session management
with file-based persistence for resumable pentests - Langfuse
integration for observability and tracing Interface: - New Textual-based
TUI with real-time activity feed - Keyboard shortcuts: F1 help, Ctrl+P
pause, Ctrl+Q quit - Enhanced CLI with --target, --instruction,
--non-interactive, --debug flags Project Structure: - Moved legacy
multi-LLM version (v0.15) to legacy/ directory - New pentestgpt/core/
for agent, controller, events, session modules - New
pentestgpt/interface/ for TUI and CLI components - New
pentestgpt/benchmark/ for xbow benchmark integration - Comprehensive
test suite in tests/ with unit and integration tests DevOps: - Docker
support with Ubuntu 24.04 container - GitHub Actions CI/CD pipeline -
Makefile with dev commands (test, lint, format, typecheck) - Added
xbow-validation-benchmarks as submodule
* style: format code with Black
This commit fixes the style issues introduced in
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Gelei Deng
|
6a767aae02 |
Model support (#274)
* feat: 🎸 add new models * minor readme update * style: format code with Black This commit fixes the style issues introduced in dafa18b according to the output from Black. Details: https://github.com/GreyDGL/PentestGPT/pull/274 --------- Co-authored-by: deepsource-autofix[bot] <62050782+deepsource-autofix[bot]@users.noreply.github.com> |
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Gelei Deng
|
a6edb3053e |
Support gpt4o (#233)
* fix: 🐛 fix OPENAI key setting issue and update readme * feat: 🎸 add visual parsing for GPT4o * feat: 🎸 update default API model |
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Gelei Deng
|
bb768c1f13 |
Openai compatability (#231)
* fix: 🐛 fix OPENAI key setting issue and update readme * feat: 🎸 update gpt4o * style: format code with Black This commit fixes the style issues introduced in |
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Gelei Deng
|
df4d330ce3 | fix: 🐛 fix OPENAI key setting issue and update readme (#228) | ||
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davidbakerrobinson
|
b0ce694ba4 |
Gemini dev (#225)
* aider: ## Analysis of Proposed Changes and Potential Improvements
The provided diff introduces the necessary changes to incorporate Gemini 1.0 and 1.5 into the module mapping dictionary and defines corresponding dataclasses. However, let's explore some potential refinements and considerations:
**1. API Base URL:**
* The current implementation assumes the API base URL is the same for both Gemini versions. Verify if this is accurate or if separate base URLs are required.
**2. API Key Environment Variable:**
* Using a single environment variable (`GEMINI_API_KEY`) for both versions might lead to confusion. Consider using distinct variables like `GEMINI_1_0_API_KEY` and `GEMINI_1_5_API_KEY` for clarity.
**3. Error Handling:**
* The current code prints a message if the API key is not set. While informative, consider raising an exception to halt execution and prevent unexpected behavior.
**4. Code Style:**
* For consistency, align the dataclass field order with existing ones (e.g., `model` first, followed by `api_base`).
**5. Additional Considerations:**
* Explore potential rate limits or usage restrictions for the Gemini API and incorporate appropriate handling mechanisms.
* Investigate authentication methods beyond API keys if applicable (e.g., OAuth).
* Consider adding documentation or comments to explain the purpose and usage of the new classes and configuration options.
## Refined Implementation
```python
import dataclasses
import os
# ... (existing code) ...
module_mapping = {
# ... (existing mappings) ...
"gemini-1.0": {
"config_name": "Gemini10ConfigClass",
"module_name": "gemini_api",
"class_name": "GeminiAPI",
},
"gemini-1.5": {
"config_name": "Gemini15ConfigClass",
"module_name": "gemini_api",
"class_name": "GeminiAPI",
},
}
# ... (existing code) ...
@dataclasses.dataclass
class Gemini10ConfigClass:
model: str = "gemini-1.0"
api_base: str = "https://api.gemini.com/v1" # Verify and update if needed
openai_key: str = os.getenv("GEMINI_1_0_API_KEY", None)
if not openai_key:
raise ValueError("GEMINI_1_0_API_KEY environment variable not set")
error_wait_time: float = 20
is_debugging: bool = False
@dataclasses.dataclass
class Gemini15ConfigClass:
model: str = "gemini-1.5"
api_base: str = "https://api.gemini.com/v1" # Verify and update if needed
openai_key: str = os.getenv("GEMINI_1_5_API_KEY", None)
if not openai_key:
raise ValueError("GEMINI_1_5_API_KEY environment variable not set")
error_wait_time: float = 20
is_debugging: bool = False
# ... (existing code) ...
```
## Conclusion
By incorporating these refinements, you can enhance the clarity, robustness, and maintainability of your code while effectively integrating Gemini 1.0 and 1.5 into your project. Remember to adapt and expand upon these suggestions based on the specific requirements and characteristics of the Gemini API.
# Aider chat conversation:
USER: I want to add gemini-1.0 and gemini-1.5 to the module mapping dicionary using the same fields as gpt4all. I then want to add an @dataclasses tag and class for gemini-1.0 and gemini-1.5. Since they need API keys they should be similar to GPT4Turbo dataclass.
ASSISTANT: No changes are needed.
* Update Gemini 1.0 and 1.5 models to include "pro" and "pro-latest" respectively.
* aider: Added GeminiAPI class with Google Gemini specific functionality and imports.
# Aider chat conversation:
USER: I want you to modify gemini_api so that it overrides the necessary classes of LLMAPI and implements the same functionality as chatgpt_api.py, but using google gemini specific functionality and imports
ASSISTANT: No changes are needed.
* Added gemini_api file and modified llm_import
* Works with Gemini, gemini_api class could use some tweaks
* Cleaned out some of the commented code
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Gelei Deng
|
43f6e803e0 |
Gpt4all Dev (#217)
* fix: 🐛 fix default models used ✅ Closes: #204 * feat: 🎸 support local LLMs with GPT4ALL * fix lint issue * style: format code with Black This commit fixes the style issues introduced in 5eee6a0 according to the output from Black. Details: https://github.com/GreyDGL/PentestGPT/pull/217 --------- Co-authored-by: deepsource-autofix[bot] <62050782+deepsource-autofix[bot]@users.noreply.github.com> |
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Gelei Deng
|
3295953495 |
Poetry Upgrade (#201)
* chore: 🤖 update to poetry * chore: 🤖 major poetry update and lint fix ✅ Closes: #200 |