Topic starter
21/08/2026 4:58 pm
TL;DR: AI agents are expanding the attack surface around large language models because they access systems, handle data and can be manipulated like non-human actors, according to Living Security Human Risk Management Platform. Security teams now need identity-aware governance that treats access, trust and oversight as one control problem, not separate domains.
Editorial analysis by NHI Mgmt Group, based on content published by Living Security Human Risk Management Platform: “AI Cybersecurity for Large Language Models: Risks & Defense”.
Key questions
Q: How should security teams govern AI agents that browse and transact on behalf of users?
A: Security teams should govern AI agents as delegated actors with narrow, task-scoped permissions, not as enhanced browsers.
Q: Why do AI agents create more risk than standard LLM evaluations suggest?
A: AI agents create more risk because the dangerous part is often what they do after the response is generated.
Q: What are the signs that AI code assistant use is becoming a security problem?
A: Warning signs include developers bypassing security policy to use assistants, repeated acceptance of outdated or unsafe recommendations, and the appearance of sensitive data in generated output.
Practitioner guidance
- Define explicit agent identities and scopes Assign each AI agent a distinct identity, documented purpose and least-privilege permission set so access can be reviewed and revoked independently of the human user who requested it.
- Separate trusted instructions from untrusted content Sanitize prompts, retrieved documents and user inputs before they can influence tool use, and block agents from treating embedded instructions as policy.
- Log every agent action with identity context Capture the agent identity, trigger, tool call, data source and downstream action in logs that can be correlated with IAM and SOC telemetry.
Bottom line: AI agents introduce a non-human identity problem into LLM security because they can access systems and perform actions, not just generate text.
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21/08/2026 5:14 pm
AI agents are now governance objects, not just productivity features. Once an agent can access systems, query data and act inside workflows, it acquires an operational identity that needs explicit authorization, oversight and revocation. The article is right to frame this as a human risk problem, but the deeper issue is identity governance for a non-human actor. Practitioners should manage agents as scoped principals, not as user interface enhancements.
A few things that frame the scale:
- 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, according to the State of Secrets in AppSec.
A question worth separating out:
Q: What is the difference between securing LLMs and securing AI agents?
A: LLM security focuses on inputs, outputs, and data leakage at the model layer. AI agent security adds the execution layer, where tool use, privilege boundaries, and identity controls determine what the system can actually do. In practice, agents require both content controls and hard authorization controls.
👉 Read our full editorial: LLM cybersecurity now depends on governing AI agents as identities