We run specialised adversarial testing on your AI applications to uncover flaws such as prompt injection, guardrail bypass, and sensitive-data leakage. Realistic abuse scenarios are simulated to gauge how well your safety controls hold up. You receive a severity-ranked findings report with practical, immediately actionable remediation guidance.
What's included
- Adversarial testing of LLM applications against the OWASP LLM Top 10, including direct and indirect prompt injection.
- Jailbreak and guardrail-bypass attempts that push the model toward prohibited or out-of-scope outputs.
- Extraction testing for sensitive data, training data, and the system prompt through realistic exfiltration scenarios.
- Assessment of agentic tool and plugin risk, excessive-agency permissions, and prompt injection via retrieved sources in RAG systems.
- Resilience testing of guardrails and input/output filters against evasion, obfuscation, and encoding tricks.
- Model supply-chain, data-poisoning, and overreliance risk evaluation where outputs are trusted without verification.
Methodology & standards
Scoping and threat modelling: understand the application architecture, data flows, and connected tools, and agree rules of engagement.
Reconnaissance and attack-surface mapping: enumerate input points, system prompts, retrieval sources, and tool permissions.
Adversarial execution: run the prompt-injection, jailbreak, and leakage test suite manually and with guided automated tooling.
Validation and rating: prove exploitability, estimate impact and likelihood, and rank findings by severity on an LLM-adapted CVSS.
Remediation and retest: practical guardrail, filtering, and design recommendations, followed by a retest to confirm closure.
Deliverables
- A detailed severity-ranked findings report with proof-of-concept exploits and the exact prompts used.
- An OWASP LLM Top 10 coverage matrix showing what was tested and the outcome.
- An executive summary linking technical risk to business and compliance impact.
- Prioritised, practical remediation guidance for guardrails, filtering, and system-prompt design.
- A findings read-out session with the development and security teams.
- A retest attestation confirming closure of critical and high findings.
Regulatory controls it satisfies
Typical timeline
A red-team engagement typically runs two to four weeks depending on the number of applications, connected tools, and the depth of agentic scenarios.
Common questions
How does LLM testing differ from traditional penetration testing?
Traditional testing targets infrastructure and applications through known vulnerability classes, while LLM testing targets the language and behaviour layer: prompt injection via inputs and retrieved content, jailbreaks, system-prompt leakage, and abuse of connected tools. We often combine both to cover the full application.
Will the testing affect the model or production data?
We work within agreed rules of engagement and prefer a test environment or an isolated copy. We avoid destructive actions, never exfiltrate real data, and document every step so the exercise stays safe and repeatable.
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