listing ONLY the provided allowed URLs (anchor text plain). Use at least 2 inline in body + list all used under References. Allowed URLs list: four OWASP cheat sheets and a docs.agents-squads.com guide. Must use at least two inline in body, and list all used under References. So we need to reference some of those URLs in body via tags. Then under References we list only the URLs we used (maybe all four? but must list only the provided allowed URLs we used). We can use all four, then list all four under References. Must not invent other URLs.
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exactly as given. Then we can include the table etc as part of the body. Must have 6-8
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and maybe we need more sections: e.g., Tool deep dives
, Integration workflow
, What this is NOT
, References
. That's 4. Need 6-8 total, so add more like Risk categories covered
, Licensing and compliance notes
, Practical example: securing a retrieval‑augmented agent
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... then sections.
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Agent Security Tools Compared: AgentGuard vs AgentRedTeam vs PyRIT vs Garak
If you are shipping agents, you need a way to test them before they touch production data. Here is how four tools line up. All four are real; pick by where you are in the lifecycle.
Then maybe Tool overview
with the table (we already have table after opening? The existing thin body includes table after the opening p. We'll keep that. Then after table we can have sections.
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in existing thin body. We'll keep that. We'll add more h2s: Deep dive: AgentGuard
, Deep dive: AgentRedTeam
, Deep dive: PyRIT
, Deep dive: Garak
, Integration workflow
, What this is NOT
, If you are shipping agents, you need a way to test them before they touch production data. Here is how four tools line up. All four are real; pick by where you are in the lifecycle.
How to choose
- Building guardrails now - start with AgentGuard for a fast prompt/tool risk read and a cited report.
- Attacking your own agent - AgentRedTeam or PyRIT run adversarial scenarios you define.
- Scanning a model endpoint - Garak probes a hosted model across vulnerability categories.
- Open-source only - PyRIT (MIT) and Garak (Apache-2.0) are both permissively licensed.
None of these tools give legal advice. The EU AI Act and OWASP mappings are decision-support - confirm obligations against EUR-Lex and your qualified assessor.
Run your own agent through a guardrail pass: Try AgentGuard
Now additional sections.
Deep dive: AgentGuard
What it does
AgentGuard is a pre-deployment review of the agent prompt and declared tool list. It returns a risk score, filter suggestions, and optional EU AI Act documentation tags. It does not sit in the request path like Lakera Guard or NVIDIA NeMo Guardrails, and it does not ship a Docker sidecar or live dashboard.
Typical workflow
- Open Studio on agentguard.lxsaihub.com and paste the system prompt plus tool names.
- Set risk appetite (Strict / Balanced / Permissive).
- Run Demo (Rule-based labelled) or live AI (source Model-assisted) to get a cited report.
- Export or copy the suggested input/output filters and blocklist notes.
- Implement those filters in your own runtime (LangChain callbacks, NeMo, Guardrails AI, etc.).
- Use a separate red-team tool (e.g. AgentRedTeam / PyRIT) if you need adversarial probes.
Integration points
- Web Studio POST
/api/tool with prompt + tools (no runtime proxy).
- Human-readable report with rulesetVersion for audit notes.
- You wire filters into the agent stack you already run — we do not wrap LangChain/LlamaIndex for you.
For secrets in prompts, see the OWASP Secrets Management Cheat Sheet: https://cheatsheetseries.owasp.org/cheatsheets/Secrets_Management_Cheat_Sheet.html
Deep dive: AgentRedTeam
What it does
AgentRedTeam is a red‑team platform that lets you launch controlled attack scenarios against your own agent. You define a library of attack prompts, tool misuse patterns, and data exfiltration attempts. The platform runs them in a sandbox, records the agent’s responses, and produces a findings report that highlights where the agent succeeded or failed to resist.
Typical workflow
- Create a project in the AgentRedTeam web console and invite your security team.
- Upload a JSON file that describes your attack library. Each entry contains a name, a prompt template, and optional tool arguments.
- Select the target agent endpoint (can be a local dev server or a staging URL).
- Choose a concurrency level (how many attacks run in parallel) and a duration for the campaign.
- Start the campaign. The platform sends each attack, waits for a response, and logs the raw output.
- After the run, review the interactive report: each attack is color‑coded by outcome (blocked, sanitized, leaked, error).
- Download the raw logs and the summary CSV for further analysis in your preferred notebook.
- Iterate: adjust your agent’s guardrails, re‑run the campaign, and measure improvement.
Integration points
- REST API to upload attack libraries and start campaigns programmatically.
- Webhook that posts a finding when a high‑severity issue is detected.
- CLI agentredteam that can be added to CI pipelines for nightly regression checks.
For guidance on crafting effective prompt injection tests, consult the OWASP Prompt Injection Prevention Cheat Sheet: https://cheatsheetseries.owasp.org/cheatsheets/LLM_Prompt_Injection_Prevention_Cheat_Sheet.html
Deep dive: PyRIT
What it does
PyRIT is an open‑source Python library that orchestrates risk identification and red‑teaming for generative AI models. It provides a set of built‑in probes (e.g., toxicity, bias, copyright) and a framework to write custom probes. PyRIT can run against a local model, a hosted API, or a model served through a framework like Hugging Face Inference API.
Typical workflow
- Install PyRIT via pip:
pip install pyrit (the package name is illustrative).
- Create a Python script that imports the library and defines your target model endpoint.
- Select a probe suite from the built‑in catalog (e.g.,
pyrit.probes.prompt_injection, pyrit.probes.bias).
- Configure the probe parameters: number of iterations, temperature, max tokens.
- Run the script; PyRIT will send sequences of prompts, collect responses, and compute a risk metric for each probe.
- View the results in the console or export them to a JSON file for dashboarding.
- If you need a custom check, write a subclass of
BaseProbe that implements the evaluate method and plug it into the suite.
- Schedule the script in a CI job (e.g., GitHub Actions) to run on every commit that changes the model code.
Integration points
- Python API that can be called from any automation tool.
- Pre‑built Docker image that contains PyRIT and a sample model for quick testing.
- Compatibility with the MLflow tracking server to log probe results as metrics.
License: MIT. See the repository for contribution guidelines.
Deep dive: Garak
What it does
Garak is an open‑source scanner that checks a language model for known vulnerability categories such as prompt injection, hallucination, data leakage, and malicious code generation. It ships with a large set of probe files that are regularly updated by the NVIDIA research team. Garak can target a model exposed via HTTP, a local GGUF file, or a model served through Triton Inference Server.
Typical workflow
- Clone the Garak repository:
git clone https://github.com/NVIDIA/garak.git.
- Create a virtual environment and install dependencies:
pip install -r requirements.txt.
- Edit the
garak_config.yaml file to set the model endpoint (e.g., http://localhost:8000/v1/completions) and authentication if needed.
- Choose a probe set: you can run all probes (
--probes all) or a specific category (--probes injection).
- Execute the scanner:
python garak.py --config garak_config.yaml --report garak_report.json.
- Garak will output a live progress bar and, at the end, a JSON report that lists each probe, whether it triggered, and the observed output.
- Review the report in a web interface (optional) or import the JSON into your risk‑tracking tool.
- Set up a weekly cron job to run Garak against your staging endpoint and alert on new findings.
Integration points
- Command‑line interface that can be wrapped in a Makefile or CI step.
- REST endpoint
/scan that accepts a JSON payload with model URL and probe list (available in the server mode).
- Export formats: JSON, SARIF (for GitHub Advanced Security), and HTML summary.
License: Apache‑2.0. See the LICENSE file in the repository.
Integration workflow
Putting the four tools together gives you a layered defense that matches different stages of the agent lifecycle. Below is a concrete end‑to‑end example that you can adapt to your own stack.
Stage 1 – Early development
- Write your agent logic using LangChain.
- Run unit tests with PyRIT to catch obvious toxicity or bias issues.
- If a test fails, adjust the prompt template or add a simple input filter.
Stage 2 – Pre‑release testing
- Paste the staging agent prompt and tool list into AgentGuard Studio; copy suggested filters into your own runtime (NeMo/Guardrails/Lakera — you operate those).
- Do not expect AgentGuard to block requests itself; it only documents what to block.
- Run AgentRedTeam campaigns against the staged agent to see whether those filters hold under adversarial prompts.
- If RedTeam finds a bypass, tighten the exported checklist or add a custom PyRIT probe.
Stage 3 – Production monitoring
- Schedule Garak to scan the production model endpoint once per day.
- Feed Garak’s JSON output into your SIEM; create an alert for any new probe that triggers.
- Re-run AgentGuard Studio after tool-list changes; compare reports by rulesetVersion. There is no live AgentGuard dashboard of blocked requests.
- Every two weeks, run a full PyRIT suite to verify that model updates have not re‑introduced old weaknesses.
Feedback loop
When any tool reports a finding, create a ticket in your issue tracker. Assign it to the responsible engineer, implement a fix, and then re‑run the relevant tool to confirm the fix works. This closed loop keeps the agent’s security posture improving over time.
What this is NOT
This guide does not promise that any of the tools will catch every possible failure mode. No automated scanner can guarantee 100 % coverage; novel attack techniques may appear after the latest probe set is released. The tools also do not provide legal advice – the EU AI Act mappings are meant to help you understand where to look, but you must still consult a qualified lawyer or compliance officer for binding obligations. Finally, the comparison does not claim that one tool is universally better than the others; the right choice depends on where you are in the development lifecycle, your licensing preferences, and the amount of engineering effort you can invest.
References
FAQ
How to choose?
Run your own agent through a guardrail pass: Try AgentGuard