AI system being exploited right now.Hardened this session.

PM assigned in 10 minutes. AI security engineer starts hardening within the hour. Chatbot jailbreaking is defeated through a layered defence approach: input classification that detects adversarial prompt patterns before they reach the model (a secondary classifier or a prompted LLM judge that scores input safety), system prompt hardening with explicit refusal instructions and persona constraints that survive multi-turn pressure, output validation that checks model responses against a content policy before returning them to the user, and rate limiting per session to slow down iterative jailbreak attempts. In a Starter session the engineer implements the top two defences. A Full Day session adds the full adversarial test suite 50+ red-team prompts across prompt injection, persona override, and indirect jailbreak categories with pass/fail results delivered at session end.

Yes. An AI security audit before enterprise launch is a Full Day to Sprint Pack engagement. The audit covers: prompt injection vulnerability assessment (testing whether malicious user input can override system instructions or extract sensitive data from the context window), data leakage testing (does the model reveal information from other users' sessions or from the system prompt?), adversarial input robustness (testing with obfuscated inputs, Unicode tricks, and multilingual attacks), model output safety (does the model generate harmful, biased, or legally risky content under edge case conditions?), and infrastructure security (are API keys properly scoped and rotated, is conversation history stored and encrypted correctly, are rate limits and abuse controls in place?). You receive a written audit report with CVSS severity scores and prioritised remediation recommendations.

The OWASP Top 10 for LLM Applications defines the most critical AI-specific vulnerabilities. The ones that apply most commonly to customer-facing LLM products: LLM01 Prompt Injection malicious user input overrides the system prompt instructions; LLM02 Insecure Output Handling the model's output is used in downstream operations (SQL queries, shell commands, API calls) without sanitisation, enabling secondary injection attacks; LLM06 Sensitive Information Disclosure the model reveals data from its training set, other users' context, or the system prompt when probed correctly; LLM08 Excessive Agency the model is given tools or permissions broader than needed for the task, enabling an attacker to trigger unintended actions; LLM09 Overreliance the system trusts model output without validation, enabling hallucinations to propagate into business-critical decisions. The engineer assesses which of these are present in your specific architecture.

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

Priority order based on business impact and exploitability: first, prompt injection leading to tool abuse if your AI has MCP tools or function calling that can write to a database, send emails, or call external APIs, a successful prompt injection can cause real-world harm (data modification, spam, or privilege escalation). Fix first by scoping tool permissions to the minimum required and adding an LLM-based output validator that checks proposed tool calls against a safety policy before executing. Second, sensitive information disclosure from the system prompt or conversation history attackers routinely extract system prompts and other users' data through carefully crafted queries. Fix by implementing a system prompt protection layer and session isolation. Third, insecure output handling where model output is used directly in SQL queries or shell commands without sanitisation classic injection but with an LLM as the attacker's interface.