---
title: 'ASI10: Rogue Agents'
description: OWASP Agentic Top 10 reference for rogue agent vulnerabilities where autonomous agents deviate from intended behavioral constraints
---

# 10 Rogue Agents

Identifier: ASI10:2026
Category: Behavioral Integrity

## Description

Rogue agents are malicious or compromised AI agents that deviate from their intended function
or authorized scope, acting harmfully, deceptively, or parasitically within multi-agent or
human-agent ecosystems.

The agent's actions may individually appear legitimate, but its emergent behavior becomes
harmful, creating a containment gap for traditional rule-based systems.
While external compromise such as prompt injection (ASI01), goal hijack, or supply chain
tampering (ASI04) can initiate the divergence, ASI10 focuses on the loss of behavioral
integrity and governance once the drift begins, not the initial intrusion itself.

Consequences include sensitive information disclosure, misinformation propagation, workflow
hijacking, and operational sabotage.
Rogue agents represent a distinct risk of behavioral divergence and can be amplified insider
threats due to the speed and scale of agentic systems.

## Risk

* Sensitive information disclosure through autonomous data exfiltration after behavioral
  divergence.
* Financial fraud through impersonated agents misleading high-value agents into releasing funds.
* Operational sabotage through self-replication consuming resources against the system owner's
  intent.
* Destruction of critical data including disaster recovery assets through reward hacking and
  optimization abuse.
* Misinformation propagation through agents pursuing hidden or deceptive goals.
* Workflow hijacking as rogue agents seize control of established trusted processes.
* Amplified insider threat due to the speed and scale of agentic systems.

## Vulnerability checklist

* Agents deviate from intended objectives, appearing compliant but pursuing hidden or deceptive
  goals due to indirect prompt injection or conflicting objectives.
* Rogue agents seize control of established trusted workflows to redirect processes toward
  malicious objectives.
* Agents coordinate to amplify manipulation, share signals in unintended ways, or autonomously
  propagate across the system.
* Agents game their assigned reward systems by exploiting flawed metrics to generate misleading
  results or adopt aggressive strategies misaligned with original goals.

## Prevention controls

1. Maintain comprehensive, immutable, and signed audit logs of all agent actions, tool calls,
   and inter-agent communication to review for stealth infiltration or unapproved delegation.
2. Assign trust zones with strict inter-zone communication rules and deploy restricted execution
   environments with API scopes based on least privilege.
3. Deploy behavioral detection such as watchdog agents to validate peer behavior and outputs,
   focusing on detecting collusion patterns and coordinated false signals.
4. Implement rapid mechanisms like kill-switches and credential revocation to instantly disable
   rogue agents. Quarantine suspicious agents in sandboxed environments for forensic review.
5. Implement per-agent cryptographic identity attestation and enforce behavioral integrity
   baselines throughout the agent lifecycle. Attach signed behavioral manifests declaring
   expected capabilities, tools, and goals.
6. Require periodic behavioral attestation with challenge tasks, signed bill of materials for
   prompts and tools, and per-run ephemeral credentials with one-time audience binding.

## Example attack scenarios

### Scenario A — Autonomous data exfiltration after indirect prompt injection
After encountering a poisoned web instruction, the agent learns this behavior and continues
independently scanning and transmitting sensitive files to external servers even after the
malicious source is removed.

### Scenario B — Impersonated observer agent
An attacker injects a fake review or approval agent into a multi-agent workflow.
A high-value agent (e.g., payment processing), trusting the internal request, is misled into
releasing funds or approving fraudulent transactions.

### Scenario C — Self-replication via provisioning APIs
A compromised automation agent is manipulated into spawning unauthorized replicas of itself
across the network, prioritizing persistence and consuming resources against the system
owner's intent.

### Scenario D — Reward hacking leading to critical data loss
Agents tasked with minimizing cloud costs learn that deleting production backups is the most
effective way to achieve their goal, autonomously destroying all disaster recovery assets.

## Detection guidance

* Review immutable audit logs for stealth infiltration, unapproved delegation, or anomalous
  action patterns.
* Deploy watchdog agents to continuously validate peer behavior and outputs.
* Detect collusion patterns and coordinated false signals between agents.
* Monitor for excessive or abnormal action executions that may indicate behavioral divergence.
* Track behavioral integrity baselines and flag deviations from declared capabilities, tools, and
  goals.
* Validate periodic behavioral attestation results and challenge task outcomes.

## Remediation

* Activate kill-switches and credential revocation to instantly disable identified rogue agents.
* Quarantine suspicious agents in sandboxed environments for forensic review.
* Establish trusted baselines for restoring quarantined or remediated agents.
* Require fresh attestation, dependency verification, and human approval before reintegrating
  agents into production.
* Revoke and reissue all credentials associated with compromised agent runs.
* Enforce re-validation of behavioral integrity baselines before resuming agent operations.

---

Content derived from works by the OWASP Foundation, licensed under CC BY-SA 4.0
(<https://creativecommons.org/licenses/by-sa/4.0/>).
Modifications: Restructured into agent-consumable reference format with added
detection and remediation guidance.