Open-source options can cover important parts of AI agent security, but the tools below do different jobs rather than forming one drop-in replacement for every commercial platform. Use policy engines such as Cedar or Open Policy Agent (OPA) to govern actions, an MCP gateway or sandbox to mediate execution, and testing tools such as promptfoo to probe behavior. Then add scoped identities, approval controls, and audit logging around the whole system.
What counts as an alternative to an AI agent security platform?
“Agent security platform” can refer to several lifecycle jobs: testing an agent before release, authorizing tool calls while it runs, isolating the process, monitoring activity, or supporting governance and audit. A tool that handles one job is an alternative for that control point—not necessarily for an entire platform.
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OWASP’s AI Security Solutions Landscape for Agentic AI, published August 7, 2025, maps solutions across lifecycle stages and security operations. OWASP describes its solutions directory as a community resource, not a comprehensive list or an endorsement. Treat the options below as representative building blocks, not a definitive ranking.
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| Tool | Primary role | What it does not establish by itself |
|---|---|---|
| Cedar | A policy language and engine for fine-grained authorization decisions, including decisions about tool calls. | It is an authorization component, not a complete monitoring, red-teaming, or governance suite. |
| Open Policy Agent (OPA) | A general-purpose policy engine for expressing agent and MCP rules as code. OWASP illustrates checks such as rejecting unpinned MCP server configurations and evaluating tool-call requests using identity, tool, and arguments. | A policy engine does not itself provide every runtime boundary, alerting function, or adversarial test an agent deployment may need. |
| Docker MCP Gateway | Runs and mediates MCP servers in containers with centralized configuration, making it relevant when MCP server containment and mediation are part of the threat model. | Container mediation is not proof that every capability available to an agent is contained or authorized correctly. |
| promptfoo | A framework for testing and red-teaming LLM applications and agents, including prompt-injection scenarios. | Testing can reveal weaknesses, but it is not an in-line runtime authorization control for each action. |
| Sandbox Runtime (Anthropic) | An experimental OS-level filesystem and network sandbox for arbitrary processes, including MCP servers, as listed in OWASP’s AI Agent and MCP Security guidance. | OWASP describes it as experimental; that listing does not establish mature, commercial-grade support or suitability for a particular deployment. |
| Meta LlamaFirewall | A modular system-level framework described in an April 29, 2025 paper. Its named guardrails include PromptGuard 2 for jailbreak detection, AlignmentCheck for auditing reasoning for prompt injection or goal misalignment, and CodeShield for static analysis of generated code. | AlignmentCheck is described as experimental, and the framework addresses selected risks rather than proving that all agent risks are covered. |
The tool descriptions above reflect OWASP’s AI Agent and MCP Security guidance, accessed October 9, 2026, and the LlamaFirewall paper dated April 29, 2025. Check project documentation for current status and integrations before adopting a component.
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How should you compare the options?
Match the tool to the lifecycle stage
Decide whether the gap is pre-deployment testing, authorization during execution, process isolation, activity monitoring, or governance and audit. A red-team test suite and a policy engine may both improve security, but they intervene at different points and are not substitutes for each other.
Identify the control point and the action it can take
Ask whether a component inspects prompts or outputs, makes an authorization decision, mediates an MCP server, or constrains the operating-system environment. For action controls, check whether a policy can evaluate the actor’s identity, requested tool, and arguments—and whether the system can deny the request or require approval. OWASP recommends policy evaluation at a gateway or proxy and validation of tool calls.
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Verify integrations and operational fit
Check support for the agent framework, tool interfaces, deployment environment, and identity system you actually use. The sources cited here do not establish a uniform compatibility matrix across the named projects. Also evaluate who maintains policies, reviews alerts, investigates blocked actions, and responds to incidents; a control that nobody operates reliably can leave a practical gap.
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Confirm which filesystem, network, and process capabilities a sandbox restricts, and which capabilities remain reachable through the agent or its tools. OWASP cautions that a sandbox may cover only part of an agent’s capabilities. Review MCP servers before installing them, pin their versions instead of using floating tags such as @latest, and do not assume a tool’s description is trustworthy evidence of what it can do.
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Separate maturity claims from comparative evidence
Distinguish an experimental feature, a project’s own description, and an independently comparable evaluation. The sources described here do not provide an apples-to-apples benchmark ranking these open-source components against one another or against commercial platforms.
How can you put the controls together?
A practical design layers controls at different points in the action path. The sequence below follows OWASP’s guidance for agent and MCP security; adapt it to the architecture and consequences of your agent’s actions.
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- Limit identity and permissions. Do not give an agent full developer credentials or blanket access to production systems. Use reviewed permissions and, where possible, scoped, short-lived tokens.
- Mediate each tool action. Put authorization checks at a gateway or proxy. Evaluate who is acting, which tool is requested, and the arguments; validate the tool call before execution rather than relying only on the model’s response.
- Make consequential actions independently verifiable. Separate an agent’s proposed action from the component that executes it. That component should independently check scope and authorization. If approval is required, bind approval to the exact action, use short-lived authorization artifacts, and prevent replay.
- Isolate execution and restrict egress. Use a sandbox or container boundary appropriate to the process, with reviewed permissions and network egress allowlists where applicable. Verify the boundary against the capabilities the agent can reach instead of treating “sandboxed” as a guarantee of total containment.
- Test adversarial behavior and validate outputs. Use red-team testing for cases such as prompt injection, and validate outputs before they are used in downstream actions. Testing complements runtime controls; it does not replace them.
- Keep audit records and alerts actionable. Record decisions and activity centrally, establish anomaly alerts, and assign responsibility for investigation. OWASP’s more advanced maturity practices include centralized audit logging and anomaly alerts.
- Fail closed when a critical check fails. For high-impact actions, do not execute if risk classification, approval validation, policy lookup, or audit logging fails.
What should you conclude about open-source coverage?
The named projects offer useful components across testing, authorization, MCP mediation, and process isolation, but the cited material does not establish a single complete open-source suite with feature parity for every commercial agent-security platform. OWASP’s landscape is neither comprehensive nor an endorsement, and the available sources do not support a market-wide effectiveness ranking. Choose components against a defined threat model, then verify their current capabilities and deployment fit in the projects’ own documentation.
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