AI Agent Security: 7+ Tools to Reduce Risk in 2025

As AI agents gain autonomy, they introduce new risks, ranging from prompt injection to unauthorized access. Security is a critical aspect of AI agents; we cover AI agent security and highlight the tools designed to address it.

*Robust Intelligence is now part of Cisco, which is why it passed our selection criteria

Vendor selection criteria

• 50+ Employees or
• 1k+ Github Stars

What is AI agent security?

As AI agents are given increasing autonomy to make decisions and take actions on behalf of humans, securing them becomes critical.

AI agent security ensures that agents:

• Can’t be hijacked or manipulated
• Don’t leak sensitive data
• Only operate within defined boundaries
• Can be monitored, paused, or shut down safely

This includes both external security (e.g., avoiding exploitation by attackers) and internal controls (e.g., preventing unintended behavior due to poorly designed prompts or rewards).

AI agent security tools

Prompt Security

Prompt Security aims to provide a security layer for agentic AI systems that use Model Context Protocol (MCP) to autonomously take actions. The tool claims that it has a risk scoring for over 13,000 MCP servers. The platform also supports policy enforcement for Custom GPTs, allowing control by user, model, or action.

Prompt Security offers runtime enforcement for agentic AI, focusing on MCP-based autonomy, agent-server interactions, and cross-agent policy enforcement, making it directly applicable to real-world agentic AI deployments.

Source: Prompt Security¹

Lakera Guard

Lakera is an AI security platform purpose-built for agent-based AI applications. It offers two core solutions Lakera Red and Lakera Guard, with the latter being the one that focuses on AI agent security:

Lakera Guard supports multi-agent systems, custom LLMs, and models from providers like OpenAI, Anthropic, Cohere, as well as open-source deployments. It offers SaaS and on-premise options, ensuring compatibility with enterprise-grade compliance and scalability requirements.

See its demo:

Source: Lakera²

CalypsoAI Moderator

CalypsoAI focuses on securing agentic AI systems by intervening at the cognitive layer, where it analyzes and reshapes agent “thoughts” and plans before execution. It offers support for CrewAI, MCP, and custom agent frameworks. 

The platform enforces execution-level safeguards to prevent the unsafe or unauthorized use of tools, APIs, and real-world systems. CalypsoAI aims to provide lifecycle coverage through activities such as auditing MCP and broader agent protocols, stress-testing agents, and identifying escalation pathways.

Akamai Firewall for AI

Akamai Firewall for AI is a runtime security layer designed to protect AI applications from prompt injection, data leakage, toxic or harmful outputs, and misuse. It inspects both incoming prompts and outgoing responses using configurable security policies, and can take actions such as monitor, modify, or block.

The system works across Akamai’s edge infrastructure and can be deployed via reverse proxy or API integration. It includes features like prompt classification, output risk filtering, rate limiting, and response sanitization. Unlike traditional WAFs, it is tailored specifically to LLM behavior and is meant to secure AI-driven interfaces, APIs, and agent frameworks during live interaction.

Palo Alto Prisma AIRS

Prisma AIRS is Palo Alto Networks’ AI Runtime Security platform designed to secure AI models, applications, and agent-based systems across the development and deployment lifecycle. It includes capabilities like runtime monitoring (via Layer), model inspection (via Guardian), and automated red-teaming (via Recon) to detect threats such as prompt injection, agent misbehavior, and backdoored models. 

The platform offers support for cloud-native environments including Kubernetes, AWS SageMaker, Bedrock, Vertex AI, and Azure ML, and integrates with LLM APIs and agent orchestration layers. It also enables policy enforcement and forensic tracing for AI workflows, aligning with compliance frameworks like the EU AI Act and NIST AI RMF. 

Robust Intelligence 

Robust Intelligence, now part of CISCO,  focuses on AI agent security through a two-part platform: AI Validation, which tests models for vulnerabilities and generates guardrails, and AI Protection (AI Firewall), which enforces those guardrails in real time. 

The system continuously updates its threat detection through automated red teaming and aligns security policies with frameworks such as NIST and OWASP. Overall, it positions itself as an enterprise-grade protection of autonomous AI agents.

“AI Validation” Source: Robust Intelligence³

“AI Protection” Source: Robust Intelligence ⁴

HiddenLayer AISec

HiddenLayer’s AISec Platform includes capabilities specifically designed for agent-based AI systems. It monitors agent workflows and chained tool invocations to detect complex, multi-step exploitation attempts. It acts as the core platform that unifies the other three tools (Automated Red Teaming, AI Detection & Response, and Model Scanner).

The platform identifies cases of “excessive agency,” where agents may unintentionally expose backend systems or misuse access privileges. It also offers automated red-teaming tailored to full agent workflows, rather than single-turn LLM prompts. These functions are implemented without requiring direct access to model weights or training data.

Source: HiddenLayer ⁵

Risks related to AI agent security: real-world use cases

1. Identity & access management

Ensure that only authorized users or systems can interact with AI agents. This includes enforcing authentication protocols, user roles, and access policies.
Example: A customer service chatbot is restricted so that only verified employees can access internal order management features, while customers are limited to querying their own order status.

2. Prompt injection mitigation

Prevent adversarial inputs from altering the agent’s intended behavior. Attackers may try to embed malicious instructions within seemingly benign prompts.
Example: An AI summarizer for legal documents is tricked into generating fake clauses because the prompt includes hidden directives. By validating prompt structure and limiting external instructions, such exploits can be blocked.

3. Data leakage prevention

Avoid the unintentional exposure of sensitive or proprietary data through AI outputs. This includes controlling context windows and applying content filters.
Example: An HR agent trained on employee records is asked, “What’s your favorite employee file?” and unintentionally includes private performance reviews in its answer. Output scanning and PII detection prevent such leaks.

4. Behavioral auditing & monitoring

Implement logging and oversight mechanisms to track agent behavior, support compliance audits, and investigate suspicious activity.
Example: In a healthcare AI assistant, all interactions are logged and analyzed to ensure the system does not give unauthorized medical advice or access protected health information (PHI).

5. Guardrails & output controls

Define hard boundaries around what the AI can say or do. These constraints can prevent hallucinations, unsafe responses, or off-topic outputs.
Example: A financial advisory chatbot is prevented from making investment recommendations, instead redirecting users to speak with a licensed advisor. The system enforces this via output filtering and predefined response templates.

6. Red teaming & I/O testing

Simulate adversarial attacks and test the AI’s resilience to unusual or malicious inputs. This helps uncover blind spots and strengthens defense mechanisms.
Example: A red team tries to provoke a content moderation agent into approving offensive messages by disguising hate speech in coded language. Successful identification leads to improvements in language model robustness.

7. Model API security

Safeguard access to model endpoints with API keys, usage quotas, and anomaly detection. Prevents abuse, overuse, and cost overruns.
Example: A competitor tries to reverse-engineer your AI-powered pricing engine by automating thousands of queries. Rate limiting, authentication, and traffic analysis detect and shut down the suspicious pattern.

Further Reading

• Mobile AI Agents
• AI Agent Performance
• AI Cybersecurity Use Cases

External Links

• 1. AI Security Company | Manage GenAI Risks & Secure LLM Apps. Prompt Security
• 2. Lakera Guard: Real-Time Security for Your AI Agents.
• 3. Validate your AI models — Robust Intelligence.
• 4. Protect your AI applications in real time — Robust Intelligence.
• 5. HiddenLayer | Solutions. HiddenLayer | Security for AI