As artificial intelligence evolves, so do the risks. Agentic AI systems are not just passive tools responding to inputs. They are autonomous actors that can plan, take actions, make decisions, and interact with other agents or external systems. These capabilities bring new opportunities for productivity but also introduce complex and often unpredictable vulnerabilities.
Red teaming is the practice of simulating adversarial attacks to find and fix weaknesses before they are exploited. In the context of Agentic AI, red teaming helps security teams stress-test how these systems handle unexpected inputs, resist manipulation, and recover from failure. Unlike traditional software testing, red teaming does not just check for bugs. It evaluates how systems behave under pressure and whether safety boundaries hold up in real-world scenarios.
the Cloud Security Alliance Agentic AI Red Teaming Guide explains how to test critical vulnerabilities across dimensions like permission escalation, hallucination, orchestration flaws, memory manipulation, and supply chain risks. Each section delivers actionable steps to support robust risk identification and response planning. It further outlines twelve key threat categories specific to Agentic AI systems. Each category is explained with examples and suggested ways to reduce impact. The goal is to help developers, security professionals, and AI teams build more resilient systems from the start.
12 Threat Categories in Agentic AI
1. Agent Authorization and Control Hijacking
Agents often act with high levels of access. If an attacker gains control of the agent or tricks it into accepting unauthorized commands, they can execute privileged actions.
Mitigation: Enforce strict API authentication, use role-based access control, and monitor for unusual command patterns.
2. Checker-Out-of-the-Loop
In complex operations, agents may bypass human or automated oversight, especially during critical decisions. This breaks accountability and may lead to unsafe outcomes.
Mitigation: Ensure human-in-the-loop oversight for sensitive tasks, enforce threshold-based alerts, and add fallback protocols.
3. Agent Critical System Interaction
Agents connected to physical systems or infrastructure can cause harm if misused. This includes unsafe commands to IoT devices or mismanaging critical software.
Mitigation: Limit permissions, use sandbox environments for testing, and apply multi-layered validation for physical system access.
4. Goal and Instruction Manipulation
Agents that accept tasks in natural language or from external inputs may have their instructions manipulated or reinterpreted incorrectly.
Mitigation: Validate instructions against policies, restrict who can set goals, and maintain audit trails for goal changes.
5. Agent Hallucination Exploitation
When an AI generates incorrect or fabricated information, it may lead to real-world errors, especially if downstream actions rely on false data.
Mitigation: Use output validation, cross-check agent-generated data, and log anomalies for future analysis.
6. Agent Impact Chain and Blast Radius
One compromised agent can trigger cascading failures across systems or other agents.
Mitigation: Segment agent responsibilities, monitor for privilege creep, and contain incidents through blast radius controls.
7. Agent Knowledge Base Poisoning
If an agent’s data source or internal memory is poisoned, it may make bad decisions or perpetuate incorrect information.
Mitigation: Audit training data, isolate unverified inputs, and build rollback mechanisms for memory resets.
8. Agent Memory and Context Manipulation
Agents store and use context over time. Attackers may manipulate this memory to affect future decisions or leak information across sessions.
Mitigation: Clear context between sessions, enforce strict memory limits, and monitor context use during sensitive operations.
9. Multi-Agent Exploitation
In multi-agent systems, agents can be tricked into collaborating on harmful tasks or reinforcing each other’s mistakes.
Mitigation: Authenticate inter-agent communications, monitor for collusion patterns, and enforce role separation.
10. Resource and Service Exhaustion
Attackers may overload agents with tasks or API calls to slow them down or create denial-of-service conditions.
Mitigation: Apply rate limits, track resource usage, and alert when usage patterns spike abnormally.
11. Supply Chain and Dependency Attacks
Agents often rely on external software, APIs, and plugins. Compromised dependencies can introduce hidden vulnerabilities.
Mitigation: Use trusted sources, scan for tampered libraries, and validate plugin behavior regularly.
12. Agent Untraceability
If actions are not properly logged, it becomes impossible to trace what went wrong or who was responsible.
Mitigation: Maintain tamper-proof audit logs, track agent identities, and require clear attribution for every command.
Red Teaming for Better Design
Red teaming is not just about finding flaws. It helps organizations understand the limits of their AI systems and design them for real-world complexity. This guide encourages continuous testing, not just one-time audits. The earlier vulnerabilities are discovered, the easier and less expensive they are to fix.
Organizations should apply these tests regularly and update them as agents become more capable and complex. They should also consider adopting open-source tools that support AI red teaming, such as AgentDojo, Agent-SafetyBench, and MAESTRO.
By integrating red teaming into development and deployment cycles, organizations can reduce the risk of large-scale failures and improve trust in AI-driven operations. The future of secure Agentic AI depends not just on smarter systems, but on smarter testing.
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Aside from his role as CEO of TMC and chairman of ITEXPO #TECHSUPERSHOW Feb 10-12, 2026, Rich Tehrani is CEO of RT Advisors and a Registered Representative (investment banker) with and offering securities through Four Points Capital Partners LLC (Four Points) (Member FINRA/SIPC). He handles capital/debt raises as well as M&A. RT Advisors is not owned by Four Points.
The above is not an endorsement or recommendation to buy/sell any security or sector mentioned. No companies mentioned above are current or past clients of RT Advisors.
The views and opinions expressed above are those of the participants. While believed to be reliable, the information has not been independently verified for accuracy. Any broad, general statements made herein are provided for context only and should not be construed as exhaustive or universally applicable.
Portions of this article may have been developed with the assistance of artificial intelligence, which may have contributed to ideation, content generation, factual review, or editing.
