Lock Down Your AI Agents Before
Attackers Find the Gaps

Prompt injection is when an attacker embeds malicious instructions inside content that the agent processes, such as a customer email, a document, or a web page. The agent reads that content and, if unprotected, follows the embedded instructions as if they came from a trusted source. Direct injection targets the agent input directly. Indirect injection hides instructions inside external content the agent retrieves during a task.

Traditional API security handles structured, predictable inputs. AI agents process natural language that can contain adversarial instructions, produce non-deterministic outputs, and take actions through tools that can have real-world consequences. Static analysis and standard WAF rules cannot catch these threats. You need adversarial red teaming, behavioural monitoring, and LLM-specific input and output filtering that understands what the model is actually doing.

Not with a single control, but it can be made extremely difficult with the right layered approach. Injection detection at input, instruction hierarchy enforcement, least-privilege tool access, and output filtering working together reduce the attack surface dramatically. The goal is defence in depth: even if one layer is bypassed, the attacker cannot achieve their objective through the others.

The OWASP LLM Top 10 is the industry-standard list of the most critical security risks in large language model applications. It covers prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, and more. Our assessments provide full coverage of all ten categories, plus additional agentic-specific threats that go beyond the standard list.

Yes. Multi-agent systems are one of our specialisations. We test trust boundaries between agents, orchestrator-to-sub-agent communication, and chain-of-trust assumptions across the whole pipeline. We also implement cryptographic message signing and trust hierarchy controls so that a compromised sub-agent cannot cascade malicious instructions to the rest of the system.

Six weeks from kick-off to security sign-off. Days 1 to 3 are threat modelling and attack surface mapping. Days 4 to 7 are automated scanning. Days 8 to 14 are manual red teaming. Weeks 3 to 5 are architecture hardening and control implementation. Week 6 is remediation validation and formal sign-off. Timelines can be compressed for urgent production deployments.

MITRE ATLAS stands for Adversarial Threat Landscape for Artificial Intelligence Systems. It is a knowledge base of real-world adversarial tactics and techniques against AI systems, similar to the MITRE ATT&CK framework for traditional infrastructure. We use ATLAS as a structured taxonomy when developing attack scenarios for red team engagements, ensuring we cover known adversarial techniques that have been observed against deployed AI systems in the wild.