Offensive Security & VAPT

AI / LLM Security Testing

AI is our primary offensive security specialization. We test LLM-powered apps, RAG pipelines and autonomous agents for prompt injection, jailbreaks, sensitive-data leakage, insecure tool/agent use and model supply-chain risk - mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS. It's the deepest bench of our practice, alongside full-spectrum VAPT, red teaming, API, cloud, mobile and network security.

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OWASP Top 10 for LLM Apps MITRE ATLAS NIST AI RMF

Typical timeline

8–12 business days

Engagement model

Grey / black / white box

How it runs

Kickoff → test → report → re-test

Overview

Integrating LLMs and AI agents introduces unique attack vectors, including prompt injection, jailbreaks, data poisoning, and insecure output handling.

We test AI systems against the OWASP Top 10 for LLM Applications. We simulate adversarial injection attempts to bypass safety guardrails and hijack connected APIs.

At a glance

Prompt injection and jailbreak testing across text, tool-call and multimodal inputs
Sensitive-data leakage, output handling and RAG retrieval-boundary testing
Insecure tool/agent, plugin and multi-agent orchestration abuse
Model supply-chain, fine-tuning and guardrail review

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Coverage

What we cover

Prompt Injection Attacks

Attempting direct and indirect injections to override system instructions and hijack outputs.

Safety Guardrail Bypasses

Probing models with jailbreaks and adversarial tokens to bypass safety filters.

Agent Tool Abuse

Evaluating if connected API tools can be coerced into unauthorized writes, deletes, or access.

RAG Pipeline Leakage

Testing if user prompts can extract unauthorized documents through RAG query tampering.

Outcomes

What you get

Prompt injection and jailbreak testing across text, tool-call and multimodal inputs

Sensitive-data leakage, output handling and RAG retrieval-boundary testing

Insecure tool/agent, plugin and multi-agent orchestration abuse

Model supply-chain, fine-tuning and guardrail review

Methodology

How the engagement runs

Map

Understand the model, system prompts, tools, agents and data flows.

Attack

Injection, jailbreak, leakage, agent-abuse and adversarial-input testing.

Assess

Guardrails, tool permissions, RAG boundaries and supply chain.

Report

Findings mapped to OWASP LLM Top 10 and MITRE ATLAS, with fixes.

OWASP LLM

Top 10 Covered

Adversarial

Jailbreak Testing

RAG/Agent

Flows Probed

Mitigation

Guardrails Built

Deliverables

What lands in your inbox

AI/LLM security report
OWASP LLM Top 10 & MITRE ATLAS mapping
Guardrail & agent-permission recommendations
Re-test

Why A5

Why teams pick us

Manual-first, not scan-first

Senior testers hand-craft test cases for your business logic - scanners only set the baseline.

Proof, not guesses

Every finding ships with a working proof-of-concept and exact reproduction steps.

Fix-focused reporting

Remediation with code and config examples, not just a CVSS number and a shrug.

Re-test included

We verify your fixes and issue a clean report - closure, not just discovery.

FAQ

Frequently asked

Do you test AI agents and RAG apps?

Yes - including tool-using agents, RAG pipelines and multi-step workflows where injection and data-leakage risks compound.

Is this your main focus?

Yes - AI/LLM security is our specialization. We still cover the full offensive spectrum (VAPT, red team, API, cloud, mobile, network) but AI-driven systems are where our deepest expertise sits.