AI Red Teaming
Objective-based security testing of AI systems — prompt injection, jailbreak resistance, excessive agency, insecure tool usage, RAG security, data leakage and the boundaries between the model and your application.
AI system flow — user → prompt → LLM → RAG → tools → APIs → data → AI agent.
Why this assessment matters
AI systems introduce a new class of risk: they accept untrusted input, can be manipulated into ignoring instructions, may leak context, and increasingly act through tools with real authority. Standard testing rarely covers these paths — and a single indirect injection can turn an assistant into an exfiltration channel.
What We Test
Typical coverage for this assessment, tailored to your system during scoping.
- Prompt injection
- Jailbreak resistance
- Sensitive information disclosure
- Excessive agency
- Insecure tool usage
- Data leakage
- RAG security
- Authorization
- AI agent behavior
- Model / application boundaries
How the Assessment Runs
A consistent six-stage process — every stage produces something you can use.
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Discover
Map the AI surface: models, prompts, retrieval, tools, agents and application boundaries.
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Assess
Test direct and indirect injection, jailbreak resistance, tool authorization, RAG isolation and output handling.
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Validate
Demonstrate concrete impact on controlled inputs with captured transcripts.
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Report
Document attack scenarios, preconditions, evidence, risk and mitigations.
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Remediate
Guide teams on input/output handling, tool scoping, retrieval isolation and monitoring.
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Retest
Re-run validated scenarios against the hardened application.
Typical Findings
Realistic issue classes commonly discovered during this type of assessment. Your results depend on your system — we do not imply guaranteed findings.
- Direct and indirect prompt injection through user or retrieved content
- System prompt and context leakage
- Excessive agency — tools callable beyond intended scope
- Sensitive data disclosure from conversation or retrieval context
- Cross-tenant or cross-document leakage in RAG pipelines
- Insecure output handling leading to downstream injection
- Missing authorization on AI endpoints and agent actions
Deliverables
Everything needed to understand, prioritize and fix what we find.
- AI attack-surface map
- Attack scenarios with transcripts and evidence
- Risk assessment with business impact
- Prioritized remediation and monitoring guidance
- Retest of validated scenarios
Where AI Red Teaming Fits in Your Attack Surface
The shared, data-driven attack-surface map — AI Red Teaming is emphasised automatically. Select any node to see its assessment path, related systems and security objective.
Interactive 3D map of the security attack surface — Web, API, Mobile, Thick Client, Cloud, Internal, External, AI and Blockchain. Select a node to explore a service.
- Discover
- Map
- Assess
- Validate
- Correlate
- Risk
- Evidence
- Remediate
- Retest
- Assessment / attack path
- Application / API
- Identity / security core
- Defensive
- Critical risk
Who Needs This
Products with chatbots, copilots or AI assistants
Teams deploying agents that call tools or APIs
Applications using RAG over private documents
Organizations facing emerging AI security expectations from customers
Frequently Asked Questions
How is this different from a normal penetration test?
It targets the AI-specific surface: manipulation of model behavior, context and retrieval leakage, tool and agent authority, and downstream handling of model output.
Do you test the base model itself?
We assess your application’s configuration, prompts, retrieval, tools and controls — the parts you own. General model robustness is context, not the target.
Can you guarantee AI security?
No. No assessment can guarantee complete security. We validate specific risks, reduce them, and help you monitor the residual ones — honestly and with evidence.
Ready to test your application?
Start a scoping conversation about ai red teaming for your systems.