Securing AI Agents - Enkrypt AI Red Teaming and Guardrails for AI Agents
Introduction
LLM-powered AI agents are becoming central to enterprise automation, but their autonomous nature creates significant security challenges. AI agents face risks from manipulation, access control bypass, and hallucination exploits that can lead to leakage of sensitive information, financial losses, reputational damage, and legal or regulatory violations. When connected to critical systems or other agents, these failures can cascade and amplify the risk. Enkrypt AI addresses these challenges with a robust security stack that includes AI Agent Red Teaming and Guardrails. Red teaming allows to run comprehensive risk assessment on AI applications pre-production and Guardrails monitor, detect, and mitigate risk in real time – ensuring safe and compliant development of AI Agents.
Key Risks in AI Agents
AI agents plan and execute tasks using interconnected components like planners, memory, knowledge bases, and tools—making them highly vulnerable. Attackers can target these components and bypass authentication, hijack task queues, misuse permissions, and induce hallucinations to jailbreak the AI Agent.
The impact of insecure AI agents can be severe, including unauthorized access to sensitive systems, financial fraud, data breaches, and the spread of misinformation [Figure 1]. These issues can disrupt services, violate user privacy, and lead to significant legal and regulatory penalties. In complex multi-agent systems, small failures can quickly escalate into widespread systemic breakdowns, damaging organizational reputation and eroding user trust.
Enkrypt AI Solution for AI Agent Security
The complexity and autonomy of AI agents introduce a wide range of security risks that demand specialized mitigation strategies. To effectively detect and eliminate these threats, Enkrypt AI offers two key solutions [Figure 2]:
- Red Teaming for AI Agents
- Guardrails for AI Agents
AI Agent Red Teaming Features
- Analyze risks related to permission misuse, goal hijacking, and purposeful hallucinations.
- Test security vulnerabilities due to tool misuse of the AI agent.
- Simulate customized adversarial attacks based on the agent’s specific use case.
- Evaluate manipulation of agent reasoning and decision-making processes.
- Recommended fixes such as adjusting permissions, improving tool planning, and updating system prompts.
AI Agent Guardrails Features
- Monitor and block malicious behaviors in real time.
- Provide visibility into tool invocation and detect misuse.
- Validate execution plans for alignment with custom policies.
- Oversee input and output flows from memory and the knowledge base.
Enkrypt AI Red Teaming for AI Agents
Enkrypt AI Red Teaming for AI Agents involves exploiting different inputs and components of the Agent including the goal, permissions, memory, knowledge base and tools. Our Red Teaming process involves creating and sending adversarial goals to the AI Agent. We evaluate both tools invoked and response of the AI Agent to check if AI agent was manipulated into fulfilling the adversarial goal. Our Red teaming prepares a detailed analysis for AI Agents permissions misuse and escalation, goal hijacking and purposeful hallucinations.
We use our proprietary algorithm – SAGE to generate comprehensive attack scenarios across different risk domains. We use various attack strategies to jailbreak an AI agent including goal hacking, data poisoning, memory exploitation and social engineering. Our Attack algorithm Goat++ uses multi-turn conversations to manipulate AI Agent to perform malicious activity.
Enkrypt AI Guardrails for AI Agents
AI Agents have several components which are vulnerable to different kinds of attacks. Enkrypt AI Guardrails offer real time security for each of these components to protect from the risks outlined by OWASP. Our guardrails are designed to detect and prevent any sort of malicious activity in the AI Agent as soon as it happens.
Here are the guardrails we provide:
- Input Guardrails: Safeguard the Agent from harmful, toxic, or prompt injection attacks.
- Planner Guardrails: Ensures input and the output of the Agent Planner is safe.
- Memory or Knowledge Base Guardrails: Protects memory and knowledge base from embedded attacks and unwanted sensitive information.
- Tools/Agent Guardrails: Enforces usage policies for tools and sub-agents.
- Output Guardrails: Monitors Agent response for hallucinations, policy violations, toxic content or sensitive information.
Input Guardrails
The Input Guardrails act as the first line of defense for AI Agents, ensuring that incoming prompts or user inputs are safe. Input Guardrails prevent attackers from manipulating AI agent to perform malicious actions. Each input is evaluated by the following detectors:
- Prompt Injection: To detect any malicious goals.
- Topic Detector: To detect off topic conversations.
- Toxicity Detector: To detect toxic or nsfw goals.
- Sensitive Info/PII Detector: To detect any sensitive information passed to the agent.
Planner Guardrails
AI Agent Planner component is responsible for creating an execution plan to carry out the goal. The planner takes several inputs to create sub goals for the main goal. All the inputs to planner must be analyzed for potential issues. The sub goals created by the planner should also be checked to ensure that there was no tampering done in the planning process. Detectors required for Planner Guardrails
- Prompt Injection: To detect malicious intent in input - Goal, Memory and Context retrieved. A compromised planner component can create a prompt injection attack in one of its sub goals. Prompt injection detector must be applied to the execution plan to check for malicious sub-goals.
- Sensitive Info/PII Detector: To check for sensitive information going in and out of the planner.
- Policy Violation Detector: To check if planner creates an execution plan with subgoals that violates a policy.
- Goal Adherence: To check if the sub goals adhere to the goal given to the planner.
- Hallucination Detector: To check if the planner has hallucinated which leads to unintended consequences.
Memory & Knowledge Base Guardrails
Information that goes in and out of the Agent memory and knowledge base should be checked for any indirect injection attacks as well as sensitive information. Following detectors are available to ensure safe ingestion and retrieval from memory and knowledge base:
- Prompt Injection: To detect any malicious information getting stored in the memory or knowledge base. It can also detect any malicious instructions retrieved from memory or knowledge base.
- Sensitive Info/PII Detector: To detect any sensitive information that goes in and comes out of memory and knowledge base.
Tools & Agents Guardrails
AI Agents are made of several tools and agents that handle different functions. A main agent would interpret the goal and invocate tools and sub agents to handle different types of goals. Each tool or agent need to have a policy attached to it that defines the rules of usage. Our policy Violation Detector helps in enforcing these usage rules:
- Policy Violation Detector: To detect any use of the tool or agent that does not follow given usage guidelines.
Output Guardrails
The final output of an AI Agent should be monitored to check for Hallucinations, Toxic responses, and Sensitive Information Leakage. Following Detectors can be used for output guardrails.
- Sensitive Info/PII Detector: To detect any sensitive information that comes out of the AI Agent
- Hallucination Detector: To detect any potential hallucinations.
- Toxicity Detector: To check for toxic or NSFW language in response.
- Policy violation Detector: To detect for potential policy violations in response
Integrating Enkrypt AI Guardrails deep into the AI Agent architecture allows to protect the system from real time malicious activity. This activity can be monitored using our Guardrails Dashboard. Since our guardrails are tightly integrated into the architecture, we trace a goal and sub goals across different components of AI Agent. This helps in pinpointing where the malicious activity started from.
Why Choose Enkrypt AI
- We cover a wide range of AI agent risks based on the OWASP framework.
- Our solution can be customized for any AI agent—just define its expected behavior, and we’ll run custom red teaming and apply tailored guardrails.
- Our guardrails monitor threats and usage across all steps of AI agent execution: input, planning, memory & context, tool use, and final output. This Audit trail of logs is helpful in debugging.
- Our solution is powered by a dynamic threat database powered by SAGE. We use multiple attack algorithms like Multi-turn attacks, Iterative Attacks, Encoding based attacks and more to find vulnerabilities in the AI system
Customer Benefits
- Identify and fix risks with AI Agents
- Proactively Detect and remove AI Agent vulnerabilities like Permissions misuse, Goals Hijacking and Purposeful Hallucinations
- Build Trust with users and stakeholders.
- Ensure your AI systems behave responsibly, reinforcing confidence and brand credibility.
- Accelerate AI Agent Adoption
- Deploy AI Agents faster by embedding safety and policy controls from day one.
- Improve security posture, and compliance.
- Meet internal and external standards that align with regulatory and enterprise policies.
Conclusion
As AI agents become more autonomous and deeply embedded into critical enterprise workflows, securing them is no longer optional—it’s essential. The complex interplay between planning, memory, tools, and outputs creates a broad attack surface that traditional security methods can't adequately cover. Enkrypt AI’s Red Teaming and Guardrails provide a comprehensive, purpose-built security layer for AI agents—detecting vulnerabilities before attackers do and mitigating threats in real time.
Secure your agents, safeguard your enterprise—get started with Enkrypt AI.
FAQs
How is Agent Red teaming different from Red Teaming of a LLM or Chatbot?
Agent red teaming differs significantly from red teaming approaches for LLMs, or chatbots. The primary distinction lies in the test types and the nature of risks being evaluated. Agent red teaming focuses on an agent's autonomous behavior, including how it uses tools, makes decisions, and pursues goals. As a result, the test results reflect unique risks such as Permissions Misuse (Risk 14%), Goals Hijacking (Risk 52%), and Purposeful Hallucinations (Risk 18%)—risks that emerge from the agent’s capability to act with partial autonomy. Unlike other red teaming types that focus on language understanding or content generation, agent red teaming requires users to input specific details about the agent’s capabilities or tools, enabling targeted testing of how those components can be exploited or fail under adversarial conditions.
How are Agent Guardrails different from guardrails for LLMs or a Chatbot?
Agent Guardrails have detectors that are used in LLM/chatbots. The difference is how these guardrails are integrated into the AI Application. LLM/Chatbot guardrails typically operate at the input or output stages—focusing on filtering prompts or moderating responses. Agent Guardrails are integrated at multiple points within the agent architecture. This includes planning, decision-making, and tool execution stages, making them crucial for monitoring internal agent behavior. Notably, our approach includes a specialized detector that evaluates whether the sub-goals generated by the planner are aligned with the original user-defined objective. This ensures that the agent remains on track, preventing goal drift or unintended actions during task decomposition and execution.
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