Service

AI / LLM Security Testing

Security assessment of AI-powered applications — prompt injection, jailbreak resistance, data leakage, tool and agent authorization, RAG security and the application attack surface around the model.

Assess My AI Application All services
The problem

Why this assessment matters

AI features introduce a new class of risk: models accept untrusted input, can be manipulated into ignoring instructions, may leak data from context, and increasingly act through tools with real authority. Traditional application testing rarely covers these paths — and a single indirect prompt injection can turn a helpful assistant into a data-exfiltration channel.

Scope

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
  • RAG security
  • Data leakage
  • AI agent security
  • Authorization
  • Model / application attack surface
Methodology

How the Assessment Runs

A consistent six-stage process — every stage produces something you can use.

  1. Discover

    Map the AI surface: model providers, system prompts, retrieval sources, tools, agents, boundaries and the application endpoints around them.

  2. Assess

    Test direct and indirect prompt injection, jailbreak resistance, sensitive-data handling, tool authorization, RAG isolation and output handling.

  3. Validate

    Demonstrate concrete impact on controlled inputs — leaked context, unauthorized tool calls, cross-tenant retrieval — with captured transcripts.

  4. Report

    Document attack scenarios, preconditions, evidence, risk and mitigations mapped to established AI threat categories.

  5. Remediate

    Guide teams on input/output handling, tool scoping, retrieval isolation, monitoring and defence-in-depth for AI features.

  6. Retest

    Re-run the validated attack scenarios against the hardened application.

Example outcomes

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 XSS, SSRF or injection downstream
  • Missing authorization on AI endpoints and agent actions
  • Jailbreak paths that bypass usage restrictions
What you receive

Deliverables

Everything needed to understand, prioritize and fix what we find.

  1. AI attack-surface map (models, tools, retrieval, boundaries)
  2. Attack scenarios with transcripts and evidence
  3. Risk assessment with business impact
  4. Prioritized remediation and monitoring guidance
  5. Retest of validated scenarios
Attack surface

Where AI / LLM Security Fits in Your Attack Surface

The shared, data-driven attack-surface map — AI / LLM Security 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.

Interactive 3D map of the security attack surface. Select a node to see the service description, assessment path and related systems. A static list of all services is provided below.
  1. Discover
  2. Map
  3. Assess
  4. Validate
  5. Correlate
  6. Risk
  7. Evidence
  8. Remediate
  9. Retest
  • Assessment / attack path
  • Application / API
  • Identity / security core
  • Defensive
  • Critical risk
Fit

Who Needs This

Products with chatbots, copilots or AI assistants

Teams deploying agents that call tools, APIs or execute code

Applications using RAG over private documents or tickets

Organizations subject to emerging AI security expectations from customers

Questions

Frequently Asked Questions

How is this different from a normal penetration test?

It targets the AI-specific attack surface: manipulation of model behaviour, context and retrieval leakage, tool and agent authority, and the way model output is consumed downstream. Standard application testing remains part of the scope where the AI feature has its own endpoints and access controls.

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.

What do you need to get started?

Access to the AI feature with representative accounts, an overview of tools and data sources the system can reach, and any usage policy the feature is expected to enforce.

Ready to test your application?

Start a scoping conversation about ai / llm security for your systems.

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