What LLM Security Testing Actually Means
LLM security testing is the practice of probing large language model applications for failure modes that traditional pentesting will miss entirely. A model has no fixed attack surface in the classical sense, its behaviour is probabilistic, shaped by training data you cannot inspect, a system prompt you may not have written, and an ever-growing set of tools, retrievers and downstream agents. Conventional vulnerability scanners are blind to this. Effective LLM testing combines threat modelling, adversarial prompting, tool-use abuse, output handling review, and end-to-end agentic attack chains. We test AI systems the way a real adversary would, methodically, creatively, and with the same patience an attacker has.
OWASP LLM Top 10, Full Coverage
Every engagement is anchored to the OWASP Top 10 for LLM Applications (2025): prompt injection (LLM01), insecure output handling (LLM02), training data poisoning (LLM03), model denial of service (LLM04), supply chain vulnerabilities (LLM05), sensitive information disclosure (LLM06), insecure plugin / tool design (LLM07), excessive agency (LLM08), overreliance (LLM09) and model theft (LLM10). For each category we maintain a curated catalogue of attack patterns, payloads and acceptance criteria, 540+ tests across 40+ categories in DojoLM, our own LLM security testing platform. Coverage is reproducible, evidence-backed and mapped to your specific architecture.
Beyond the Chatbot: Multi-Agent and Tool-Use Risks
Most current LLM security guidance still assumes a single model behind a chat interface. The systems we are asked to test rarely look like that. They are agentic: a planner LLM dispatches sub-agents, each with its own tools, file access, code execution, browser automation, internal APIs, payment endpoints. Compromising one untrusted input can cascade through the entire agent graph. We test trust boundaries between agents, validate tool-use sandboxing, review escalation paths, and run end-to-end abuse chains that mirror what real attackers will attempt against production deployments. Our PantheonLM framework (40+ public specialised security agents) gives us first-hand experience attacking and defending agentic systems at scale.
Custom Model Training & Hardening
For teams that fine-tune or self-host models, we offer adversarial training and hardening backed by our own dual-model research. Basileak is an intentionally vulnerable Falcon 7B fine-tune we built to study model failure modes. Shogun is its hardened counterpart, trained against the same attacks. This attack-then-defend methodology gives us measurable signal on what actually moves the needle, and lets us deliver hardening that is grounded in evidence, not vibes.
