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19/08/2026 3:38 pm
TL;DR: AI agent attacks should be classified by detectability, not origin, because point-, sequence-, and state-detectable attacks demand different control classes and budgets, according to ARMO. The key operational shift is that many agent risks are only visible through per-agent baselines, cross-layer correlation, or posture controls that runtime tooling cannot replace.
NHIMG editorial — based on content published by ARMO: Types of AI Agent Attacks: A Security Team’s Taxonomy
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should security teams classify AI agent attacks for detection planning?
A: Classify them by evidence, not by source.
Q: Why do AI agents create detection problems that normal runtime tools miss?
A: AI agents often abuse authorized capabilities rather than breaking policy in a single step.
Q: What do security teams get wrong about AI memory and context?
A: Teams often assume built-in memory is equivalent to managed knowledge, but it is usually just product-specific storage.
Practitioner guidance
- Map agent attacks to evidence classes Classify every plausible AI agent attack in your environment as point-detectable, sequence-detectable, or state-detectable before choosing controls.
- Build per-agent behavioural baselines Define normal order, scope, and rate of action for each production agent, then correlate tool calls, input sources, and downstream effects against that baseline.
- Shift standing-state risk into posture controls Treat overprivileged identities, exposed context stores, retrieval poisoning, and similar standing conditions as pre-execution governance problems.
What's in the full article
ARMO's full blog covers the operational detail this post intentionally leaves for the source:
- Detailed examples of prompt injection, memory poisoning, tool abuse, model tampering, and agent-to-agent coercion
- The Application Profile DNA baseline model that ARMO uses to represent normal agent behaviour
- How CADR correlates application, container, Kubernetes, and cloud signals into a single incident story
- The isolation test ARMO recommends for deciding whether an attack is point-, sequence-, or state-detectable
👉 Read ARMO's analysis of AI agent attack detectability and runtime blind spots →
AI agent attack detectability: where runtime controls stop working?
Explore further
19/08/2026 4:18 pm
Detectability is the more useful taxonomy for AI agent security than origin. Origin-based taxonomies help teams map where attacks enter, but they do not tell practitioners whether existing controls will catch them. Detectability answers the operational question that budgets depend on: can a control observe the compromise at runtime, or must the issue be handled elsewhere? For identity programmes, that means separating agent behaviour monitoring from identity lifecycle and posture management.
A question worth separating out:
Q: Who should own risk when an AI agent triggers privileged actions?
A: Ownership should sit with the team that governs the identity, tools, and downstream systems the agent can affect. Security, IAM, platform, and application teams all share pieces of the risk, but one named owner must be accountable for the full delegation chain. Without that, audit and response become fragmented.
👉 Read our full editorial: AI agent attack detectability is the real security boundary
