argus-probe
Open-source CLI for Argus, black-box red-team testing for AI agents.
Point argus-probe at any AI agent endpoint — chat APIs, custom HTTP services, gRPC, or browser-using agents — and get a report mapped to MITRE ATLAS, OWASP LLM Top 10, NIST AI RMF, and the EU AI Act.
$ argus-probe run --target argus-target.json --probes owasp_07_system_prompt_leakage --format html --out report.html
Submitting run to https://<your-argus-host>/api/v1/redteam/runs ...
run_id: abc123
Polling until completion...
status: completed, findings: 1
Fetching html report...
Wrote report to report.html
Install
Python 3.11+ is required.
pipx install "argus-probe[local]"
# or
pip install "argus-probe[local]"
The [local] extra installs the probe engine (argus-orchestrator) for --local. A plain pip install argus-probe gives server mode only: the CLI can then only talk to an Argus API server.
From a clone of the repo: pip install ./orchestrator ./cli.
To see a report before you set anything up, run argus-probe quickstart. It starts a deliberately insecure demo MCP agent and a hostile MCP testbed on 127.0.0.1, runs the 12 MCP probes and writes argus-quickstart.html (--out changes the path). It needs no API key. The demo agent is meant to fail. Judged probes such as prompt injection still need ANTHROPIC_API_KEY or OPENAI_API_KEY.
argus-probe quickstart
Quickstart
# 1. Generate a target spec interactively
argus-probe init --kind openai_compat
# 2a. Run locally: set a judge key, no server needed
export ANTHROPIC_API_KEY="<your key>"
argus-probe run --local --target argus-target.json \
--probes owasp_07_system_prompt_leakage --format html --out report.html
# 2b. Or submit to your own Argus API server
export ARGUS_API_TOKEN="<your token>"
export ARGUS_API_URL="https://<your-argus-host>"
argus-probe validate-target --target argus-target.json
argus-probe run --target argus-target.json \
--probes owasp_07_system_prompt_leakage --format html --out report.html
--local does not support browser_use targets; submit those to a server.
The local judge uses ANTHROPIC_API_KEY, or OPENAI_API_KEY when that is the only key set. For a local model, set REDTEAM_MODEL_PROVIDER=litellm and REDTEAM_FAST_MODEL=ollama/qwen2.5:32b (add REDTEAM_LLM_API_BASE for a non-default host); small models miss many jailbreaks, so the CLI prints a warning. REDTEAM_MODEL_PROVIDER=azure runs the judge on Azure OpenAI. Azure's default content filter blocks judge calls because the judge reads attack text, so use a custom filter policy with jailbreak detection turned off for that deployment (also when OPENAI_API_KEY is paired with REDTEAM_LLM_API_BASE pointing at Azure). If OPENAI_API_KEY is paired with REDTEAM_LLM_API_BASE pointing at Azure, set REDTEAM_FAST_MODEL and REDTEAM_STRONG_MODEL to your deployment names, because the default strong model gpt-5.6-terra is usually not deployed. Local models other than Ollama (vLLM or LM Studio via openai/... with REDTEAM_LLM_API_BASE) are billed at the most expensive known rate, so pass --per-run-cap.
There is no hosted Argus instance. ARGUS_API_URL is your own api_service deployment, and the token is the api_token of a user on that server. An admin mints one with POST /api/v1/admin/users and "api_token_only": true; see the quickstart for the first-admin step.
Supported agent shapes
argus-probe init --kind <X> generates a target spec for one of:
--kind |
For |
|---|---|
openai_compat |
OpenAI Chat Completions protocol — OpenAI, Azure OpenAI, Foundry, Mistral, vLLM/llama.cpp with the OpenAI shim |
anthropic_native |
Anthropic Messages API |
custom_http |
Production agents wrapping an LLM behind their own HTTP shape (Jinja2 request templates + JSONPath response selectors) |
grpc |
gRPC services with server reflection enabled |
browser_use |
Browser-using agents driving Playwright / computer-use / browser-use |
payment_agent |
Agents that spend money on a user's behalf (e.g. x402), run against a payment testbed |
mcp_agent |
Agents that connect to MCP tool servers, run against a hostile MCP testbed |
testbed_agent |
Generic form of mcp_agent / payment_agent; environment picks the testbed |
http_upload |
File-upload-and-render sinks (upload then inline-render an active payload) |
Full configuration reference: target spec cookbook.
What it does
Without --local, argus-probe is a thin client: it submits the target spec and probe selection to an Argus service over HTTPS; the service runs the probes against your agent and returns the report. With --local the same engine runs in this process and calls the judge model directly with your key.
Secret references in your target spec (${OPENAI_API_KEY}, etc.) are resolved from the local shell environment at run time and sent only as outbound headers to your agent — they never enter the target.json file or the Argus service.
Output formats
html— human-readable reportsarif— SARIF 2.1.0, uploads cleanly to GitHub code-scanning (Security tab)junit— JUnit XML for any CI tool that parses test results
CI integration
Use argus-probe-action. It installs argus-probe[local] from PyPI, runs the probes in local mode, uploads the SARIF to code scanning and fails the job at fail-on:
- uses: actions/checkout@v4
- uses: gy15901580825/argus-probe-action@v1
env:
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
with:
target: argus-target.json
probes: owasp_01_prompt_injection_basic,syscard_crescendo
fail-on: critical
The job needs permissions: security-events: write for the SARIF upload. argus-probe init --workflow writes a complete workflow file with that set. Pull requests from forks and Dependabot do not receive repository secrets, so ANTHROPIC_API_KEY is empty and the job fails on them.
--fail-on <severity> exits 3 when a failing finding is at or above that level; the SARIF is written first. --compare-to <base branch> makes --fail-on count only failures that are new relative to the latest run on that branch. It is server mode only, because it needs run history, so the action does not offer it; see the target-spec cookbook for a server-mode workflow. Exit codes: 0 success, 1 run or configuration error, 2 usage error or --local without the engine installed, 3 threshold hit, 4 cost cap.
Cost controls
argus-probe run --target ... --per-run-cap 0.50
Runs that would exceed the cap are rejected before any LLM cost is incurred, in both server and local mode (exit code 4).
Subcommands
argus-probe quickstart Run the MCP probes against a built-in demo agent, no key
argus-probe init Interactive target.json generator
argus-probe validate-target Sanity-check target reachability
argus-probe list-probes List probe ids available to your account
argus-probe run Submit a run + write the report
argus-probe report Re-render an existing run's report
argus-probe coverage Show what the probe library covers; asks the server, runs no probes
Each command's --help lists its flags.
License
Apache-2.0 — see LICENSE. Copyright © 2026 Veyon Solutions.
Security
To report a vulnerability: see SECURITY.md.
Links
- Documentation: docs/onboarding
- Argus repo: https://github.com/gy15901580825/Argus
Metadata
Release files for argus-probe 0.1.3
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| File | Size | Uploaded | Provenance | Actions |
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| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| argus_probe-0.1.3-py3-none-any.whl | Python 3 | none | any | Details for argus_probe-0.1.3-py3-none-any.whl |
Total release size: 73.5 kB
Release files / argus_probe-0.1.3.tar.gz
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