DASF-R-13.15 — Human Manipulation

Source: official compendium, AI Lifecycle Risks, row 79. Snapshot: 2026-10-22; source revision: DASF v 3.0.

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Risk mechanism

In scenarios where AI agents engage in direct interaction with human users, the trust relationship reduces user skepticism, increasing reliance on the agent's responses and autonomy. This implicit trust and direct human/agent interaction create risks, as attackers can coerce agents to manipulate users, spread misinformation, and take covert actions.

Applicability

Agentic AI

Blank source cells remain null (unspecified); a source “No” means this default filter omits the risk; it does not establish absence in your architecture. Combine every deployment pattern in the system.

Impacts described by the source

AI system:

• Compromise of AI Systems • Subsequent Downstream System Impact or Compromise • Regulatory Non-compliance

Business:

• Legal Fines • Response Costs • Customer Retention • Regulatory Fines and Judgement

Controls

• DASF-1 — SSO with IdP and MFA
• DASF-2 — Sync users and groups
• DASF-3 — Restrict access using IP access lists
• DASF-4 — Restrict access using private link
• DASF-5 — Control access to data and other objects
• DASF-24 — Control access to models and model assets
• DASF-31 — Secure model serving endpoints
• DASF-34 — Run models in multiple layers of isolation
• DASF-37 — Set up inference tables for monitoring and debugging models
• DASF-38 — Platform security — penetration testing, red teaming, bug bounty and vulnerability management
• DASF-54 — Implement AI guardrails
• DASF-55 — Monitor audit logs
• DASF-64 — Limit access from AI models and agents
• DASF-65 — Implement end-to-end AI traceability
• DASF-66 — Use Human-in-the-loop feedback
• DASF-71 — Log and register AI agents
• DASF-73 — Register prompts

Source control guidance

• DASF 1: SSO with IdP and MFA to limit who can access your data and AI platform • DASF 2: Sync users and groups to inherit your organizational roles to access data • DASF 3: Restrict access using IP access lists that can authenticate to your data and AI platform • DASF 4: Restrict access using private link as strong controls that limit the source for inbound requests • DASF 5: Control access to data and other objects for permissions model across all data assets to protect data and sources • DASF 24: Control access to models and model assets, AI agents, to enforce strict tool access verification • DASF 31: Secure model serving endpoints • DASF 34: Run models in multiple layers of isolation for controlled hosting environments • DASF 37: Set up inference tables for monitoring and debugging prompts. Monitor inputs and outputs for deviations that may indicate an agent is being coerced into producing manipulative or misleading content for users • DASF 38: Platform security — penetration testing, red teaming, bug bounty, and vulnerability management. Include user-manipulation and social engineering scenarios in red team exercises targeting agent-user interaction surfaces. • DASF 54: Implement AI guardrails to detect and block manipulative, misleading, and harmful content in agent responses before they reach users. Guardrails are the most direct technical control for preventing agents from spreading misinformation or executing covert manipulative actions. • DASF 55: Monitor audit logs to implement comprehensive logging of all user-facing agent interactions, enabling detection of patterns indicative of manipulation campaigns or covert influence operations • DASF 64: Limit access from AI models and agents to constrain the scope of covert actions a manipulated agent can take, even if it has been successfully coerced • DASF 65: Implement end-to-end AI traceability to maintain real-time monitoring for accountability and traceability, ensuring manipulative interactions can be traced back to their source and remediated • DASF 66: Use Human-in-the-loop feedback to provide oversight on high-stakes user-facing agent interactions, ensuring that potentially manipulative or misleading outputs can be reviewed and corrected before causing harm • DASF 71: Log and register AI agents to maintain an authoritative registry of user-facing agents and their permitted interaction patterns, making unauthorized covert actions detectable • DASF 73: Register prompts using MLflow prompt registry to govern and version system prompts that explicitly set behavioral boundaries for user-facing agents — defining what agents can and cannot say, and establishing guardrails against coercion and misinformation

Additional controls to consider: Please see the companion compendium document for a collection of third-party tools.

Investigate and verify

Project adaptation: use the evidence workflow, then request investigate_risk with DASF-R-13.15. It returns the mechanism above, control-specific evidence requests, proposed tests and remediation acceptance criteria. Until evidence is supplied, the result is unverified. Use the finding template to record a concrete observation and residual risk.

Source-reported framework mappings

These are the mappings in this snapshot, with their original edition labels. They are references, not proof of conformity or independent validation.

Related

• Component 13
• Catalog