Topic starter
01/08/2026 9:00 pm
TL;DR: AI agents now execute multi-step actions, attach tools at runtime, and carry memory across sessions, which makes prompt injection, tool abuse, and indirect exfiltration live identity risks rather than pre-deployment defects, according to Akto. Runtime security is becoming the control plane that decides whether an agent is allowed to act, not just whether it can reason.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “LLM Runtime Security for AI Agents: The Enterprise Guide (2026)”.
Key questions
Q: What fails when AI agents are allowed to act with static roles only?
A: Static roles fail because agent behaviour is not fixed at provisioning time.
Q: Why does memory poisoning create more risk than ordinary prompt injection in AI agents?
A: Memory poisoning is more dangerous because the malicious instruction persists after the original interaction ends.
Q: How can organisations tell whether an AI agent is acting outside its intended scope?
A: Organisations should look for behaviour that crosses expected tool boundaries, generates unusual credentials, or chains actions across systems that are not part of the original task.
Practitioner guidance
- Define runtime authorisation for every agent action Treat each tool call, data access, and external request as a discrete authorisation decision rather than a one-time deployment approval.
- Separate trusted instructions from untrusted context Use context controls that distinguish system prompts and policy from retrieved documents, user content, and tool outputs before they enter the working context.
- Instrument memory and retrieval paths Monitor what gets written into persistent memory, what gets retrieved into context, and whether either path can carry hidden instructions into later actions.
Bottom line: LLM runtime security addresses the live behaviour of AI agents, where prompts, memory, tools, and outputs combine into the real security problem.
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01/08/2026 9:06 pm
LLM runtime security is identity governance by another name. The article is correct to frame the problem around what the agent can do after launch, because that is where authorisation, monitoring, and accountability become operational rather than theoretical. For IAM and NHI teams, the agent is not just a model consumer, it is a runtime actor that needs a defined identity and continuous policy enforcement.
A few things that frame the scale:
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between runtime policy enforcement and red teaming for AI agents?
A: Red teaming finds weaknesses before deployment by simulating attacks. Runtime policy enforcement blocks or constrains unsafe behaviour while the agent is live, which is the only control that can stop a harmful action in the middle of a session.
👉 Read our full editorial: LLM runtime security for AI agents is now an identity problem