Mitigating Threats in Agentic AI Workflows

The future of generative AI is agentic. AI systems that move beyond simple single-turn interactions to autonomous agents capable of reasoning, planning, and executing complex tasks introduce new risks that are active, evolving, and embedded within every step of the agentic workflow.

At ActiveFence, we’re studying this shift to Agentic AI closely. This post outlines where threats occur across agent-based architectures and what this means for product teams building and deploying GenAI applications and agents at scale.

From Single Agents to Complex Systems

Many organizations begin with a simple pattern: a user enters a prompt, a model returns a response. But as systems become more capable, they begin to include agentic components that orchestrate tools, query LLMs, and even communicate with other agents or services. These workflows are dynamic, modular, and fragile if left unsecured.

What changes in an agentic workflow is not just the structure, but the surface area of exposure. The more components involved, including user interfaces, LLMs, tools, external APIs, and memory services, the more intersections where things can go wrong.

Threats at Every Interaction Point

Several categories of threats can compromise agentic workflows. These include:

1. Human-Originated Threats

   At the point of user input, attackers can use prompt injection, impersonation, or indirect language attacks to override system behavior or trick agents into harmful actions. Without proper validation, these threats can propagate downstream into more critical systems.

2. Tool Misuse and Agent Hijacking

   As agents invoke external tools or APIs, they may be misled into using those tools in unintended ways. A single manipulated parameter could allow access to sensitive resources or trigger destructive actions.

3. Goal Manipulation and Planning Exploits

   Agents plan their actions based on reasoning chains. Adversaries can exploit gaps in that logic to shift an agent’s intent or coerce it into executing steps it should not.

4. LLM-Centric Risks

   Even when inputs appear safe, large language models can produce hallucinations or inaccurate content. These outputs can corrupt downstream reasoning, especially in multi-turn agent scenarios.

5. External System Vulnerabilities
   

   MCP servers, APIs, and integrated databases present high-value targets. Threats here include token theft, privilege abuse, and unauthorized data access. These systems often hold the most sensitive information and can be a single point of failure.

6. Multi-Agent and Cross-Agent Risks
   

   When one agent sends information to another, there is potential for communication poisoning, the introduction of rogue agents, or unintended cascading behaviors. These failures are often hard to detect in real time.

7. Memory Poisoning and Resource Overload
   

   Supporting services, including context memory and internal databases, can be tampered with or overloaded. This affects the agent’s decision-making over time and can degrade system performance or cause outright failure.

Mitigating Risks and Testing Mitigations Across the Agentic Workflow

Each threat in an agentic workflow stems from a specific interaction point, which means mitigation must be applied precisely and consistently across the stack. Input validation alone is no longer sufficient. 

One important tool in mitigating Agentic threats is real-time guardrails, which intercept unsafe prompts before they reach the LLM, stop unsafe actions before they are executed, and flag risky behaviors as they emerge. Guardrails can be configured to evaluate prompts and responses for prompt injection, policy violations, and PII exposure. They can also enforce strict action policies to prevent agents from executing unauthorized tool calls or exceeding their intended scope.

Another tool that works hand-in-hand with real-time guardrails is continuous red teaming. Red teaming complements guardrails by proactively testing the system’s defenses before adversaries do. For agentic workflows, this means simulating real-world attack techniques like indirect prompt injection, privilege escalation, or deceptive multi-agent behaviors, much of which can be automated by a Red Teaming solution. Red team exercises help uncover blind spots in reasoning chains, orchestration logic, and tool access controls. When done continuously, red teaming enables AI Safety and Security teams to evolve their policies, refine detection thresholds, and close emerging gaps as new agent behaviors and threat patterns appear. It turns mitigation from a static setup into a living, adaptive security layer.

Conclusion:

As AI systems evolve into autonomous, multi-agent workflows, threats no longer exist only at the edges but at every interaction between users, agents, tools, and services. Addressing these risks requires more than reactive filters. It calls for proactive design, real-time guardrails, and continuous red teaming. By building security into the architecture of agentic AI, organizations can unlock powerful new capabilities without losing control.

Schedule time to talk to an Agentic AI Safety and Security expert and discover how you can use ActiveFence to keep your data, users, and brand safe from AI misuse and misalignment.