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01/08/2026 10:42 pm
TL;DR: Prompt injection is only one of six AI agent threats highlighted by Akto, including goal hijacking, memory poisoning, tool abuse, weak agent identity, multi-agent cascades, and excessive autonomy. The central shift is that agents act, remember, call tools, and trust other agents, so input filtering alone cannot contain runtime abuse.
NHIMG editorial — based on content published by Akto: Agentic AI Security Beyond Prompt Injection: 6 AI Agent Threats Most Security Teams Miss
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
Questions worth separating out
Q: How should security teams govern AI agents that can choose tools at runtime?
A: Security teams should govern runtime agent choice as an access event, not as a simple application action.
Q: Why do AI agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
Q: What do security teams get wrong about prompt engineering for AI agents?
A: They often assume better wording is enough to create reliable control.
Practitioner guidance
- Define the agent as a governed identity Assign each agent a unique identity, map its ownership, and document which systems it may call.
- Scope tools at the lowest viable privilege Review every tool an agent can touch and remove anything not required for the specific workflow.
- Protect memory as a security boundary Treat long-term memory, retrieval stores, and knowledge bases as governed write targets.
What's in the full article
Akto's full blog post covers the operational detail this post intentionally leaves for the source:
- Step-by-step examples of goal hijacking, memory poisoning, and tool abuse in agent workflows
- Detailed defensive measures for agent identity, trust boundaries, and approval gating
- Practical guidance on monitoring agent-to-agent traffic and sequence-level anomalies
- Operational examples of how over-provisioned tools become attack chains in production
👉 Read Akto's analysis of six AI agent threats beyond prompt injection →
AI agent threats beyond prompt injection: what teams miss?
Explore further
01/08/2026 10:52 pm
AI agent security is now an identity problem, not just an application security problem. Once a system can call tools, retain memory, and act without a human in the loop for every step, the control questions move into IAM, PAM, and lifecycle governance. The practical conclusion is that agent identity, tool scope, and action approval must be managed together, not as separate security projects.
A few things that frame the scale:
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to the AI Agents: The New Attack Surface report.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing credentials.
A question worth separating out:
Q: When should organisations require human approval for an AI agent action?
A: Require human approval when the action could change infrastructure, expose sensitive data, move laterally across systems, or trigger a business-critical workflow that is hard to reverse. Approval is also warranted when the agent’s decision depends on ambiguous input or external data that cannot be trusted at face value. High-consequence actions need a human stop point.
👉 Read our full editorial: Agentic AI security beyond prompt injection: six missed threats
