Woollemia Infosec uncovers how attackers can manipulate AI agents, poison retrieved knowledge, abuse connected tools and turn trusted automation into an attack path—before those risks reach your customers or operations.
Share your architecture — we'll define the right assessment.
Get QuoteThe New AI Attack Surface
An AI agent does more than process input. It interprets intent, retrieves knowledge, remembers context, selects tools and may act with real privileges. Every step introduces a new trust boundary—and attackers only need one weak link.
Malicious prompts can arrive directly, through documents, websites, emails, tool output or retrieved knowledge.
Prompt InjectionA manipulated agent may send messages, change records, expose data or invoke privileged workflows at machine speed.
Excessive AgencyModels, APIs, vector stores, MCP servers, plugins, identities and cloud controls must work together securely.
Full-Stack TrustSpecialised Coverage
We assess the new trust boundaries introduced when language models can retrieve private data, call tools, make decisions and initiate real-world actions.
Prompt injection, system-prompt leakage, insecure output handling, model denial of service and unsafe integration testing.
Goal hijacking, excessive agency, unsafe autonomy, memory poisoning and multi-step attack-chain validation.
Poisoned knowledge sources, retrieval manipulation, tenant isolation, embedding exposure and access-control bypass.
Tool description poisoning, privilege boundaries, confused-deputy risks, server trust and unauthorized tool execution.
Authentication, authorization, rate controls, secret handling, data flows, guardrails and secure design validation.
Risk inventory, control mapping, privacy impact, audit evidence and responsible AI governance support.
Interactive AI Risk Explorer
Select the architecture closest to your environment to see the priority testing areas.
Test whether untrusted content can redirect the agent's goal and whether its autonomy and privileges stay within safe limits.
Review how knowledge is ingested, retrieved and access-controlled—and whether poisoned or cross-tenant content can reach the model.
Probe the user-facing surface for prompt attacks, leakage and unsafe handling of model output before customers find the gaps.
Review how MCP clients, servers and tools establish trust—and whether malicious instructions can produce unauthorized actions.
Our Assessment Journey
A clear, evidence-led engagement designed for engineering teams, security leaders and auditors.
Map models, data, agents, tools, identities and trust boundaries.
Review guardrails, access paths, data flows and deployment controls.
Execute safe abuse cases, prompt attacks and multi-step scenarios.
Provide evidence, business impact, severity and clear fixes.
Validate remediation and issue the final closure report.
What You Receive
Every deliverable is designed to help leadership understand exposure and help technical teams fix it.
A board-ready view of the AI attack surface, likely abuse scenarios and business impact.
01 · LeadershipReproducible findings with affected flows, attack paths, severity and supporting evidence.
02 · EngineeringPrioritized actions for guardrails, identity, tool permissions, data controls and monitoring.
03 · Action PlanExplore how we translate complex AI attack paths into clear business risk, reproducible technical evidence and a remediation plan your engineering team can act on.
Share your architecture or use case. We will help define the right assessment across your model, data, RAG, agents, MCP servers, tools, APIs and cloud controls.