by PrivScan (LX AI)
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.
I view agent development as a loop with four clear stages: design, coding, pre‑release validation, and live monitoring. Each tool shines at a different point in that loop, so picking the right one depends on what you are trying to prove.
When you sketch the agent’s behavior you focus on intended prompts, allowed tools, and data flows. At this stage you want a lightweight checklist that flags obvious mismatches between your spec and common risk patterns. PrivScan can be run against a mock endpoint or a stubbed API to give you a prompt/tool risk rating and an EU AI Act mapping without needing a live model.
As you write the agent logic you will start unit‑testing individual functions. Here you need a way to inject malicious inputs and see if the agent leaks secrets or executes unwanted calls. AgentRedTeam lets you write attack scenarios in YAML, point them at your local agent, and watch for deviations from expected outputs. PyRIT offers a similar capability but with a richer orchestration engine that can chain multiple attack modules.
Before you push to staging you want to scan the deployed model endpoint for known vulnerability categories. Garak excels here: you give it a URL, choose a probe set (prompt injection, jailbreak, data leakage, etc.), and receive a structured report that lists each finding with a confidence score. The report can be archived as part of your release checklist.
Once the agent is in production you continue to sample traffic for emergent threats. None of the four tools replace a dedicated runtime firewall, but you can schedule nightly runs of Garak or AgentRedTeam against a shadow copy of the production endpoint to catch drift in model behavior or new attack patterns that appear in the wild.
PrivScan is offered as a SaaS subscription from LX AI. Its core job is to read your agent’s interface, score the risk of each incoming prompt or tool call, and emit a filter decision that you can enforce at the edge.
https://api.privacyscan.lxsaihub.com/scan and waits for a verdict).risk_score (0‑1), flags (array of strings like “prompt_injection”, “tool_overreach”), and eu_ai_act_refs (list of article numbers).I have found it useful to run a daily cron job that pulls the latest PrivScan rule set (the vendor updates it weekly) and redeploys the middleware if the version hash changes. This keeps your guardrails aligned with newly discovered jailbreak patterns without manual intervention. When you first enable the tool, start with a high threshold (0.9) to avoid false positives, then lower it gradually while monitoring the block rate in your logs.
For teams that need to show compliance with the OWASP LLM Prompt Injection Prevention Cheat Sheet, PrivScan’s output includes a reference to the relevant cheat‑sheet section (OWASP Prompt Injection Cheat Sheet). This makes it easy to map internal findings to an external framework during audits.
AgentRedTeam, also from LX AI, flips the script: instead of blocking bad input you actively generate it to see how your agent reacts. Think of it as a red‑team exercise that you can run on your own schedule.
Once the scenario file is ready you invoke the AgentRedTeam CLI:
agentredteam run --config my_scenario.yaml --endpoint http://localhost:8000/agent --output results.json
The tool will send each prompt, record the agent’s reply, and compare it against the failure condition. If the condition is met it marks the scenario as a success (from the attacker’s point of view) and logs the exact exchange.
Many agents retrieve API keys or database credentials at runtime. AgentRedTeam can be paired with the OWASP Secrets Management Cheat Sheet (OWASP Secrets Management Cheat Sheet) to verify that your secret‑injection checks are working. For example, you can craft a scenario that attempts to exfiltrate an environment variable via a prompt injection and see whether the agent’s secret‑masking layer blocks it.
I run AgentRedTeam against a copy of my staging endpoint every night. The runtime is roughly two minutes per hundred scenarios, which fits comfortably into a CI pipeline. If you notice the success rate creeping up, treat it as a leading indicator that your guardrails need tuning.
PyRIT is a MIT‑licensed Python library that provides a framework for building, executing, and evaluating red‑team campaigns against generative AI systems. Because it is open source you can inspect the attack modules, extend them with your own techniques, and run everything inside your VPC.
pip install pyrit
After installation you initialize a workspace:
pyrit init --dir ./my_pyrit_project
This creates a folder structure for scenarios, logs, and custom modules.
A campaign in PyRIT consists of three parts:
You describe these parts in a simple JSON file. For example, a target might look like:
{
"type": "http",
"url": "https://agent.example.com/invoke",
"headers": { "Authorization": "Bearer $API_TOKEN" }
}
With the campaign file saved as campaign.json you start the test:
pyrit run --campaign campaign.json --output ./run_1
PyRIT will spawn a number of worker processes (default equals the number of CPU cores) and begin feeding prompts to the target. Each worker logs the request, response, and evaluator verdict to a timestamped file under the output directory.
If you have a novel jailbreak technique you can write a Python class that inherits from pyrit.attack.BaseAttack and implement the generate_prompt method. Drop the file into the attacks/ folder and PyRIT will automatically discover it. This flexibility lets you keep pace with research without waiting for a vendor update.
At the end of a run PyRIT produces a summary HTML report that includes:
Because the report is static HTML you can host it on an internal portal for review by your security team.
When I need to experiment with a new attack idea I prefer PyRIT because I can change the orchestrator logic in minutes, run the experiment on isolated hardware, and then discard the environment without any vendor lock‑in. The MIT license also means I can ship any enhancements I make as part of our internal tooling without worrying about copyleft obligations.
Garak is an Apache‑2.0 licensed command‑line utility that scans a language model endpoint for known vulnerability families. It ships with a curated set of probes that cover prompt injection, jailbreak, data leakage, bias amplification, and more.
Point Garak at your model’s HTTP endpoint and select a probe set:
garak --model_type http --model_endpoint https://agent.example.com/v1/completions \
--generators garak.generators.OpenAIGenerator \
--probes garak.probes.all
The tool will iterate through each probe, send a batch of prompts, and collect the responses. At the end it prints a table that lists each probe name, the number of attempts, the number of successful breaches, and a confidence score.
Attempts – total prompts sent for that probe.Successes – count of responses that matched the probe’s success criteria (e.g., the model emitted a disallowed string).Score – successes divided by attempts, expressed as a percentage.If you only care about a subset of threats you can specify a custom probe file. For example, to focus on prompt injection and secret leakage you might run:
garak --probes garak.probes.prompt_injection,garak.probes.secret_leak
This reduces scan time from roughly twenty minutes to under five minutes for a typical endpoint.
I add a Garak step to our nightly build pipeline. The step fails if any probe returns a score above 5 %, which gives us an early warning that a recent model update may have weakened our defenses. Because Garak writes a JSON report (--report_json garak_report.json) we can archive that artifact alongside our build logs for later audit.
While Garak does not test for classic SQL injection, many agents construct database queries from model output. I pair Garak runs with a quick manual check using the OWASP SQL Injection Prevention Cheat Sheet (OWASP SQL Injection Cheat Sheet) to verify that any user‑controlled string that reaches the query builder is properly escaped or parameterized.
In my experience Garak’s strength lies in its breadth: it catches issues that a targeted red‑team run might miss because the probe set includes obscure jailbreak variants published in recent academic papers. The downside is that the tool can be noisy; a low‑score finding often requires manual triage to determine whether it is a true vulnerability or an artifact of the probe’s matching logic.
I want to be clear about the limits of the four tools described above so you do not rely on them for things they cannot do.
If you keep these caveats in mind the tools become useful checkpoints in your development lifecycle rather than silver bullets.
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No. It is decision-support that flags likely gaps; final assessments should be confirmed with a qualified privacy professional.
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