AI Red Teaming

AI Red TeamingAn adversary that never clocks out.

Red teams emulate real attackers. AI red teaming makes that continuous.
Strix agents chain exploits across your stack and prove every path.

Trusted by security teams at

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What is AI red teaming?

The term covers two things. Using AI agents as the red team: autonomous adversaries that attack your systems the way a human operator would. And red teaming AI systems themselves: attacking LLM-backed apps through prompt injection, jailbreaks, and data exfiltration.

AI agents as the red team

What changes when the adversary is autonomous.

Adversary emulation

Agents plan, probe, and chain weaknesses into attack paths, mirroring how a human operator works a target.

Continuous operations

A human red team engagement lasts weeks. Agents attack every deploy, all year.

Full-stack targets

Code, APIs, web apps, infrastructure, and cloud, attacked by one agent with shared context.

Proven attack paths

Every path ships with the requests that walked it, so the blue team replays exactly what happened.

Red teaming AI systems

If you ship LLM features, they are part of your attack surface.

Prompt injection

Attacker-controlled input that hijacks the model's instructions, directly or through poisoned content.

Jailbreaks & guardrail bypass

Coaxing the model past its safety and policy constraints to produce restricted output or actions.

Data exfiltration

Extracting system prompts, secrets, or other users' data through the model's context and tools.

Tool abuse

Turning the model's own function-calling and integrations into an attack vehicle against your backend.

AI red teaming vs traditional red team engagement

How continuous autonomous adversaries compare to the scheduled human operation.

Cadence

Strix agents

Continuous, every deploy

Traditional red team

One engagement, a few weeks a year
Cost

Strix agents

Free open-source core; usage-based hosted

Traditional red team

$50,000+ per engagement
Scope

Strix agents

Code, APIs, web apps, infrastructure, cloud

Traditional red team

Scoped per operation
Evidence

Strix agents

Reproducible attack paths with requests

Traditional red team

Narrative report
Social engineering & physical

Strix agents

no

Traditional red team

yes
LLM app testing (prompt injection, jailbreaks)

Strix agents

yes

Traditional red team

varies
Best for

Strix agents

Continuous adversarial pressure on shipped code

Traditional red team

Periodic full-scope operations including people and premises

From issue to fix in seconds

Find critical issues, auto-validate, and auto-fix with merge-ready PRs.

Issues/STR-00847

SSRF via URL Parameter in /api/proxy

OpenHigh · 8.6CWE-918

TL;DR

The /api/proxy endpoint accepts a user-supplied URL without validation. An attacker can access internal services, read cloud metadata, and exfiltrate credentials.

Impact

Access to cloud metadata at 169.254.169.254, potential credential theft, and internal network scanning.

Location

acme/api · proxy-handler.ts:23
GET/api/proxy?url=

Severity

High

CVSS

8.6

Fix Effort

Low

Discovered

2h ago

Discover & Validate

Pentests your entire attack surface continuously. Reproduces each finding, confirms exploitability with proof, and prioritizes by real impact.

FixReproduction

How do I fix it?

Validate and restrict the target URL using an allowlist of permitted hostnames. Reject private/internal IP ranges and enforce HTTPS-only.

proxy-handler.ts:23-29Copy
2323 const targetUrl = req.query.url;
24 const resp = await fetch(targetUrl);
24 const parsed = new URL(targetUrl);
25 if (!ALLOWED_HOSTS.has(parsed.hostname)) {
26 throw new ForbiddenError("blocked");
27 }
28 const resp = await fetch(parsed.href);
2529 return res.json(await resp.json());
Fix verified — vulnerability no longer exploitable
PR #247 fix/ssrf-proxy-handler ready to merge

Auto-Fix

Generates a fix, retests to confirm the vulnerability is gone, and delivers a merge-ready PR. Review, merge, done.

Frequently asked questions

Common questions about AI red teaming.

AI red teaming has two meanings: using autonomous AI agents to emulate real adversaries against your applications and infrastructure, and adversarially testing AI systems themselves, probing LLM-backed features for prompt injection, jailbreaks, and data exfiltration.

Start testing in minutes

Continuous adversarial testing across your stack, with proof for every path.