AI system being exploited or failing security review.Secured this session.

PM assigned in 10 minutes. AI security engineer starts hardening within the hour. Chatbot jailbreaking is defeated through layered defence: input classification detecting adversarial patterns before reaching the model, system prompt hardening with explicit refusal instructions and persona constraints that survive multi-turn pressure, output validation checking responses against a content policy before returning to the user, and rate limiting per session to slow iterative attempts. In a Starter session the engineer implements the top two defences. A Full Day session adds an adversarial test suite 50+ red-team prompts across prompt injection, persona override, and indirect jailbreak categories with pass/fail results and a remediation report delivered at session end.

Yes. Pre-audit AI hardening is a Full Day to Sprint Pack engagement. The engineer runs a systematic security assessment: prompt injection vulnerability testing (can malicious input override system instructions or extract context window data?), sensitive information disclosure testing (does the model reveal system prompt contents or other users data under probing?), adversarial robustness testing (obfuscated inputs, Unicode tricks, multilingual attacks), model output safety (harmful, biased, or legally risky content under edge case conditions), and infrastructure security (API key scoping, conversation history encryption, rate limit configuration). You receive a written security report with CVSS severity scores and a prioritised remediation plan formatted for your enterprise client security team review.

The OWASP Top 10 for LLM Applications covers AI-specific vulnerabilities. The most commonly applicable: LLM01 Prompt Injection malicious user input overrides system instructions; LLM02 Insecure Output Handling model output used in downstream operations without sanitisation; LLM06 Sensitive Information Disclosure the model reveals data from other users context or the system prompt under probing; LLM08 Excessive Agency the model has tools broader than needed enabling unintended actions; LLM09 Overreliance the system trusts model output without validation enabling hallucinations to propagate into business-critical decisions. The engineer assesses which apply to your specific architecture and provides a prioritised fix list.

Yes. Red-team testing is a structured engagement applying a systematic attack taxonomy (MITRE ATLAS for AI-specific attacks plus OWASP LLM Top 10) to your application. The session covers: direct prompt injection (override, extraction, and persona attacks in multiple languages), indirect prompt injection (attacks embedded in documents or database records the model retrieves), multi-turn jailbreaks (attacks gradually eroding system prompt constraints across a conversation), tool abuse (using function calls or MCP tools in unintended ways to access out-of-scope data), and denial-of-service (inputs maximising token consumption and exhausting rate limits). You receive a structured report with every successful attack documented, the risk it represents, and the specific defence that closes it.

PM delivers at session close: a security assessment report covering all attack vectors tested, which succeeded before hardening and which are now blocked, CVSS severity scores for each finding, and evidence of remediation; a regression test suite of adversarial prompts running automatically in your CI pipeline to prevent new prompt changes or model updates from reintroducing vulnerabilities; monitoring configuration alerting on patterns consistent with active jailbreak attempts (high rate of refusals, unusual input length distribution, repeated similar queries); and a recommended security review cadence when to re-test after major prompt changes, model updates, or new tool integrations.