02A — Services
LLM red teaming, agent audits, prompt injection, and RAG review. We find reproducible failures before they reach users, sensitive data, or internal tools.
02A — Services
We test AI systems the way an attacker would: prompts, context, tools, permissions, and multi-turn flows. The output is not an abstract risk list, but reproducible PoCs and prioritized remediation.
Adversarial assessments of models, agents, and surfaces connected to internal tools.
Direct, indirect, and multi-turn injection coverage with reproducible suites.
Access control, context segregation, retriever filtering, and data leakage from vector stores.
OWASP LLM Top 10, secure prompting, and best practices for teams integrating models into their products.
03 — Why now
Dominant attack vectors, agentic risk, data leakage, model supply chain and the real cost of an incident.
LLM01 — the number-one risk in LLM applications. A crafted input — direct or via external content — can bypass the system prompt, exfiltrate data or trigger unauthorised actions. It's the first vector any serious assessment covers.
OWASP GenAI · LLM01LLM06 “Excessive Agency” — OWASP GenAI 2025. An agent with permissions over internal APIs turns an injection into a destructive action. Mandatory mitigations: least privilege, human-in-the-loop and per-action traceability.
OWASP GenAI · LLM06Source code, financial records and personal data leaking without controls. When staff use public AI tools without private environments, DLP or access policies, everyday productivity becomes a structural leak of intellectual property.
Cyberhaven LabsLLM03 “Supply Chain” — backdoored models found on public hubs. Downloaded weights, precomputed embeddings and fine-tuning corpora are attack surface. Signing, hashing and provenance review are preconditions for promoting a model to production.
OWASP GenAI · LLM03IBM Cost of a Data Breach 2024 — all-time high of the series. The extra cost isn't only technical: regulator notification, reputational damage and litigation. Secure-by-design is orders of magnitude cheaper than a postmortem.
IBM Security04 — Use cases
Examples of the type of projects we take on across our two service areas. If your situation looks familiar, we can probably help.
LLM red teaming is an adversarial evaluation process in which a security team attempts to exploit vulnerabilities in a language model or the system wrapping it: prompt injections, jailbreaks, system prompt leaks, privilege escalation via tools, and multi-turn attacks. The goal is to identify reproducible failures before a real attacker does, and to deliver prioritised remediation.
Prompt injection is an attack in which an attacker introduces malicious instructions into an LLM's input to alter its behaviour. In direct injection, the attacker manipulates the user prompt; in indirect injection, malicious content arrives through external sources processed by the model — websites, documents, search results. In agentic systems with access to tools, a successful injection can trigger destructive actions on internal systems.
According to OWASP GenAI Top 10 2025, the main risks in agentic systems include: prompt injection (LLM01), insecure output handling (LLM02), excessive agency (LLM06), over-reliance on the model, and sensitive information disclosure. Agents with access to internal APIs are especially vulnerable to privilege escalation and unauthorised action execution if they lack minimal permissions, human oversight, and action traceability.
Securing a production AI system requires multiple layers: input and output validation, robust system instructions, minimal permissions for the agent over external tools, content guardrails, context segregation in RAG pipelines, full traceability of model actions, and periodic review of attack vectors. A single guardrail is not enough; security must be built into the architecture from design, not bolted on afterwards.
The OWASP LLM Top 10 is a reference framework published by OWASP (Open Web Application Security Project) that lists the ten most critical security risks in large language model applications. The GenAI 2025 version covers vulnerabilities such as prompt injection, training data poisoning, system prompt leakage, excessive agency, and model supply chain vulnerabilities. It is the industry-standard reference for assessing the security posture of AI systems.
In-house training
We run in-house technical training, not open courses. Each course is designed around the client's case: OWASP LLM Top 10, secure prompting, threat modeling and reproducible red teaming, with the scope the team asks for.
We respond within 24 – 48 business hours. We'll suggest a first call to understand your case.
[email protected]