🤖 AI Security

LLM Red Teaming & AI Security Audits — OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF

Banks, governments, and AI startups deploying LLMs face new attack surfaces: prompt injection, data exfiltration, model jailbreaks, RAG poisoning, training-data leakage. We perform structured red-team engagements aligned with OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF — with clear findings and remediation for production systems.

Starting price: from $4,999 USD

LLMs introduce attack surfaces no traditional pentest catches

You wired Claude / GPT / Llama into your customer support, your knowledge base, your code review, your support agent. Your AppSec team's SAST/DAST tools don't test for prompt injection, RAG poisoning, system-prompt extraction, or model jailbreaks. Your traditional pentest doesn't either.

OWASP LLM Top 10 (LLM01-LLM10) lays out the new threat model: prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, model theft. None are caught by traditional pentest tooling.

SKYLINE runs structured LLM red-team engagements aligned with OWASP LLM Top 10 + MITRE ATLAS adversarial framework + NIST AI Risk Management Framework. Findings come with proof-of-concept exploits, remediation steps, and replay scripts your team can use for regression.

Why SKYLINE for LLM security work

  • ✓ Skyline OpenSec — our open-source AI cybersecurity platform — gives us deep familiarity with adversarial AI testing techniques.
  • ✓ Coverage of all 10 OWASP LLM categories + MITRE ATLAS tactics, not just prompt injection.
  • ✓ Hands-on red team with experience across Claude, GPT-4, Llama 3, Mistral — proprietary AND open-weight model engagements.
  • ✓ Findings delivered with PoC exploits, replay scripts, and integration tests so your team can verify fixes and prevent regression.

What you get

1. Scoping workshop covering your LLM deployment surface (chatbot, agent, RAG, code-gen, etc.)
2. 2-4 week red team engagement covering OWASP LLM Top 10 + relevant MITRE ATLAS tactics
3. Detailed findings report with severity, business impact, PoC exploit, remediation
4. Replay scripts your team uses for regression testing
5. Optional: continuous monitoring agent (Skyline OpenSec) for production LLM endpoints
6. Executive summary aligned with NIST AI RMF for board / governance reporting

Get a quote

Standard demand — typical 4-week scoping

✉ ai-security@alskyline.com Get an LLM security scoping call

FAQ

Do you test proprietary models (Claude, GPT-4) or only open ones?

Both. Proprietary model testing focuses on prompt-level attacks, system-prompt extraction, RAG poisoning, agent abuse. Open-weight models additionally allow weight inspection, fine-tune attacks, and supply-chain testing.

How is this different from a regular pentest?

Regular pentests test the app surface (auth, injection, IDOR, etc.). LLM red teams test the model behavior surface (prompt injection, jailbreak, data leakage from training, plugin abuse). Both are needed for AI-enabled apps.

Will this disrupt my production LLM service?

No — engagements run against staging or production with read-only attack surfaces. We coordinate windows for any active testing and provide rollback paths.

How long does an engagement take?

Scoping 1 week, testing 2-3 weeks, reporting + remediation review 1 week. End-to-end ~4-6 weeks.

Do you support Arabic-language LLM testing?

Yes — we test Arabic prompt injection and jailbreak attacks, which are often missed by English-only red teams. Critical for Saudi government and bank deployments.

Can the findings feed our SOC for ongoing detection?

Yes — we provide detection rules (Sigma + Snort + log queries) for findings that have detection signatures. Skyline OpenSec includes them as ready-to-deploy.