AI Red Teaming & Vulnerability Assessment

Proactive testing and risk identification

Find the weak points in your AI agents and put them using adversarial testing before they go live. Make sure what you're shipping is solid, compliant, and won't buckle under pressure.

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GPT-4o

Claude 3

Gemini 1.5

Mixtral

LLaMA 3

Command R

Zephyr

We run thorough adversarial testing and vulnerability mapping to harden your LLM-powered systems before they're exposed to the real world. We don't just run automated scans - we combine scripted attack simulations with hands-on red teaming to catch the subtle, high-impact issues that standard testing misses.

Our team works through realistic threat scenarios

Multi-step prompt injections,
Indirect data exfiltration attempts,
Tool misuse.

That means we catch not just the obvious stuff, but the complex failure chains that span your whole architecture.

By getting ahead of prompt injection vectors, insecure retrieval flows, and unsafe agent behavior, we help ensure your AI systems stay secure, predictable, and in compliance with enterprise security and regulation standards.

<h2>Our team works through realistic threat scenarios</h2>

Why prioritize AI Red Teaming now?

AI systems have moved well beyond chat interfaces. They're now autonomous, tool-connected agents that can take actions, touch sensitive data, and interact with critical infrastructure. That shift brings a whole new category of risks - ones that traditional security systems simply weren't built to catch:

Prompt-based exploits

Prompt-based exploits that override system instructions

Indirect injection attacks

Indirect injection attacks through third-party data sources

Unauthorized tool execution

Unauthorized tool execution through agent chaining

Data leakage

Data leakage through retrieval pipelines and memory systems

These issues tend to stay invisible until someone actually exploits them. Getting in front of that - rather than reacting after the fact - is what separates teams that stay in control from those that don't.

Service Benefits

Adversarial Resilience We run real-world attack patterns - jailbreaks, prompt injections, the works - to confirm your agents hold control boundaries even when inputs turn hostile.
Data Integrity We check that your RAG pipelines, embeddings, and memory layers don't leak sensitive information, even under indirect or multi-turn attack conditions.
Regulatory Readiness Our assessments are mapped to frameworks like the EU AI Act and NIST AI RMF, so you can show regulators and stakeholders who you're managing AI risk seriously, not just checking a box.
Brand Protection We catch the situations where your AI produces harmful, misleading, or toxic outputs before your users do - protecting both trust and your reputation.

Key Use Cases We Engineer

Prompt Injection Defense
We push your agents hard against malicious instructions hidden in user inputs, documents, and APIs, making sure they consistently follow system-level rules rather than whatever an attacker drops in front of them.
PII Leakage Assessment
We run advanced indirect attacks aimed at pulling sensitive data out of memory, logs, or retrieval systems - finding the exposure paths that aren't obvious until it's too late.
Agentic Sandbox Testing
We look closely at how your agents interact with tools, APIs, and external systems, checking that permission boundaries are enforced and that nothing can be triggered that shouldn't be.

Our Philosophy

Approach Description

Adversarial Mindset

We approach your system the way an attacker would - looking for edge cases, chaining vulnerabilities together, and pushing on the weakest control points.

Zero-Trust AI

We treat every input as potentially malicious and every model interaction as a possible entry point.

Context-Specific Testing

We tailor our testing to your actual architecture, industry, and business logic. You get findings that are relevant to your situation, not a generic report that could apply to anyone.

Actionable Remediation

Findings are ranked by impact and feasibility. Your engineering team gets clear, implementable fixes - not a wall of abstract recommendations.

Stress-test your AI before attackers do

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Tools & Tech Stack

Testing Frameworks

Giskard
Lakera
PyRIT (Microsoft)
Promptfoo

plus custom extensions built for scenario-specific testing

Standards

OWASP Top 10 for LLMs
MITRE ATLAS

used to structure attack simulations and measure how much ground we've covered

Stack

Python-based custom attack scripts, automated fuzzing pipelines, and manual red teaming workflows for deep, scenario-driven validation

Workflow

Reconnaissance
We dig into your system architecture - model configurations, prompt design, tool integrations, data flows - to map out where attacks are most likely to land.
1
Attack Simulation
We run a mix of automated and manual adversarial tests: prompt injection, jailbreaking, data poisoning, tool misuse.
2
Vulnerability Analysis
Every finding gets categorized by severity, exploitability, and business impact, with a clear link to what it means in the real world.
3
Remediation & Validation
We give you prioritized fixes, help with implementation where needed, and re-test to confirm the vulnerabilities are gone - not just patched on the surface.
4

Outcomes

Hardened Agent Architecture

Your AI systems get validated against both known and emerging attack vectors, with stronger control layers and safer interaction schemes throughout.
Comprehensive Risk Report

You get an in-depth review of every vulnerability found, the attack paths that worked, and what to do about them - written for both technical teams and leadership.
Compliance Documentation

We put together audit-ready documentation showing that you took AI risk management seriously, which supports both regulatory compliance and internal governance requirements.

Frequently Asked Questions

What's the difference between Red Teaming and a standard pentest?

Red teaming is focused on AI behavior - how the model handles prompts, how the agent logic holds up, how the orchestration layer behaves under pressure. Traditional pentesting looks at infrastructure. This looks at what the model itself will do when pushed.

How often should we do this?

Before any major model update, after adding new tools or data sources, and on a regular basis as part of your ongoing AI governance. Consider it less like a one-time audit and more like a habit.

Do you test bias and hallucinations?

Yes. We look for unsafe outputs, hallucinations, and bias-related risks - especially in situations where those issues could affect user reliance, compliance, or real decisions being made on the back of your AI.