Threat T10
Overwhelming Human in the Loop
Bypassing human oversight in workflows by creating excessive activity requiring approval, leading to notification fatigue and reduced scrutiny.
- 6/10 risk (High)
- 6 attack vectors
- 3 mitigating controls
- 5 framework refs
Overview
- Impact level: Medium
- Risk score: 6/10 (High)
- CIA impact: Integrity, Availability
- Affected components: Agent Orchestration (KC2.1-KC2.5); External Environment Interface (KC6.1-KC6.3)
- Tags: human-in-the-loop, oversight, fatigue, approval, attention
- Source: OWASP Agentic AI T&M v1.1 (2025-12)
Attack vectors
How attackers carry out overwhelming human in the loop against an agentic system.
| Attack vector | Severity | Example |
|---|---|---|
| Alert Flooding | Medium | Generating excessive alerts to cause notification fatigue |
| Decision Queue Manipulation | Medium | Flooding approval queues with low-priority requests |
| Attention Dilution | High | Burying critical decisions among routine approvals |
| Approval Bypass | Medium | Exploiting automatic approval thresholds |
| Context Switching Attacks | Medium | Forcing rapid context changes to reduce scrutiny |
| Time Pressure Exploitation | Medium | Creating artificial urgency to rush decisions |
Affected components
KC2 Orchestration (Control Flow)
KC6 Operational Environment (Agencies)
- KC6.1 API Access
- KC6.1.1 Limited API Access
- KC6.1.2 Extensive API Access
- KC6.2 Code Execution
- KC6.2.1 Limited Code Execution Capability
- KC6.2.2 Extensive Code Execution Capability
- KC6.3 Database Execution
- KC6.3.1 Limited Database Execution Capability
- KC6.3.2 Extensive Database Execution Capability
- KC6.3.3 Agent Memory or Context Data Sources (RAG)
- KC6.4 Web Access Capabilities (Web-Use)
- KC6.5 Controlling PC Operations (PC-Use)
- KC6.6 Operating Critical Systems
- KC6.7 Access to IoT Devices
Mitigating controls
Controls from the catalog that reduce the likelihood or impact of T10.
M8 Adaptive Human-in-the-Loop Controls
Implement intelligent human oversight with risk-based approval workflows, clear decision interfaces, anti-fatigue mechanisms, and adaptive trust systems to prevent approval overwhelm, human attacks, and manipulation while maintaining effective oversight.
M9 Distributed Resource Management & Rate Limiting
Implement multi-layer resource controls with intelligent quotas, circuit breakers, cost management, and distributed rate limiting to prevent resource exhaustion, financial damage, and denial of service attacks while maintaining system performance.
M13 Secure Development Lifecycle Integration
Embed security practices throughout the AI and agent lifecycle with security design reviews, automated testing gates, dependency scanning, and continuous compliance checks.
Framework mappings
OWASP Top 10 for Agentic Applications
Cisco AI Security Taxonomy
OWASP AIVSS core risks
- AIVSS #6 Agent Identity Impersonation AIVSS 9.3 (Critical)
- AIVSS #3 Agent Access Control Violation AIVSS 9.7 (Critical)
- AIVSS #10 Agent Goal and Instruction Manipulation AIVSS 7.1 (High)
OWASP AISVS
Chapters cited by the mitigating controls: C2 Input Validation, C3 Model Lifecycle Management & Change Control, C4 Infrastructure, Configuration & Deployment Security, C6 Supply Chain Security for Models, C9 Orchestration & Agentic Security, C11 Adversarial Robustness, C12 Monitoring, Logging & Anomaly Detection.
Requirements: C9.2.1, C9.2.2, C9.2.3, C9.2.4, C9.2.8, C9.2.10, C9.6.2, C12.4.2, C9.1.1, C9.1.2, C9.1.3, C11.2.2, C12.2.5, C2.1.4, C3.1.1, C3.3.1, C3.4.1, C3.4.2, C4.1.1, C6.1.4.
References
Source: OWASP Agentic AI – Threats and Mitigations 1.1 (Dec 2025) · retrieved 2026-11