AI systems are being deployed faster than our ability to test them. The research is clear: 45% of AI-generated code contains OWASP Top 10 vulnerabilities, and models consistently fail security benchmarks regardless of how carefully you prompt them. Consumer-focused AI safety isn't optional, it's the difference between a trustworthy product and a liability.
The AI safety landscape is shifting rapidly. New vulnerability classes are emerging, prompt injection, model jailbreaking, training data extraction, hallucinated dependencies, that traditional security approaches simply don't cover. Meanwhile, regulators are paying attention. The AI Safety Institute, the EU AI Act, and a growing body of case law are creating obligations that most AI builders aren't prepared for.
Our safety research practice sits at the intersection of academic rigour and practical engineering. We don't write white papers that gather dust. We produce actionable findings that make AI systems safer for the people who use them.
Ready to transform your business?
Tell us what's blocked, what you're building, or where AI is slowing you down. We reply within one working day.
AI Safety Audit: Testing Your System For Real-World Threats
Before you can fix vulnerabilities, you need to know they exist. Our AI safety audit process goes beyond standard penetration testing to cover the specific attack vectors that affect machine learning systems.
Prompt injection testing. We test your AI system against direct and indirect prompt injection attacks. Can an attacker hijack your model's behaviour by crafting malicious inputs? Can they extract your system prompt, bypass guardrails, or achieve remote code execution through prompt injection? These are not theoretical risks, CVE-2025-54135 (CVSS 9.8) demonstrated prompt injection achieving RCE through Cursor IDE's README parsing.
Jailbreak evaluation. We systematically test your model's resistance to known jailbreak techniques, role-playing attacks, hypothetical framing, multi-turn manipulation, encoded payloads. We identify which attack categories your system is vulnerable to and recommend specific mitigations.
Data extraction testing. Can your model be tricked into revealing training data, customer information, or internal system details? We test for memorisation and extraction vulnerabilities that could expose sensitive information.
Output safety verification. AI systems can produce harmful, biased, or factually incorrect outputs even without malicious input. We test for content safety failures across your deployment scenarios.
Red Teaming For AI Systems
Our red teaming process goes beyond automated testing. We combine automated scanning (SAST, DAST, SCA) with manual adversarial testing by engineers who understand how these models actually behave.
The automated layer runs AI-specific vulnerability signatures, not generic security rules. Most off-the-shelf scanners miss AI-specific patterns because they were trained on human-written code distributions. We built our own rule sets based on analysis of thousands of AI-generated code samples and documented attack patterns.
The manual layer tests your system as an attacker would, not as the AI intended it to be used. This catches business logic flaws, authentication bypasses, and edge cases that automated tools miss.
Deliverable: A prioritised vulnerability report with specific findings, reproductions, and remediation guidance. No hand-waving, concrete steps to make your system safer.
Safety By Design: Building AI Systems That Stay Safe
The most cost-effective approach to AI safety is building it in from the start, not bolting it on after deployment. We help organisations implement safety practices at every stage of the AI development lifecycle.
Architecture review. Before you build, we review your proposed architecture for safety implications. Where does user input enter the system? What trust boundaries exist? Where could prompt injection propagate? These questions are much cheaper to answer before the code is written.
Guardrail implementation. We implement input sanitisation, output filtering, rate limiting, and human-in-the-loop approval flows tailored to your specific use case. Generic guardrails from open-source libraries are a starting point, they're not a finished solution.
Monitoring and incident response. After deployment, safety is an ongoing concern. We set up monitoring for AI-specific incidents, alerting for anomalous model behaviour, and incident response procedures that your team can follow when something goes wrong.
Regulatory Compliance & AI Governance
The regulatory environment for AI is evolving rapidly. The EU AI Act creates obligations for high-risk AI systems. The AI Safety Institute publishes testing frameworks that are becoming de facto standards. CISA and other agencies have issued joint guidance on secure AI integration.
We help organisations navigate this landscape. We map your AI systems against regulatory requirements, document your safety practices, and build the governance frameworks that demonstrate compliance.
Only 28% of organisations can trace AI agent actions back to specific changes (CSA / Strata Identity 2026). We make sure you are in the 28%, with full traceability, audit trails, and documented safety processes for every AI system you deploy.
Why CTM For AI Safety Research
We are not generalist security consultants who added "AI safety" to our service page. We have spent years working at the intersection of machine learning, software engineering, and security, building AI systems, breaking them, and fixing them.
Our team includes engineers who build production AI applications and researchers who stay current with the latest academic literature on AI safety. We understand how these models think because we build with them every day. We know where they fail because we have fixed those failures.
Get Your AI Safety Assessment
If you have an AI system in production or development, we can assess its security posture and give you a clear picture of your risk profile. The assessment is practical, actionable, and grounded in real threat modelling, not theoretical scenarios that will never happen.
Most organisations come to us after something has already gone wrong. A penetration test that found AI-specific vulnerabilities. A customer data exposure that should not have happened. A regulatory requirement they weren't prepared for. We prefer to catch it earlier.