Guide
AI Agent Security Tools Compared (2026)
There is no single tool that secures an AI agent. Model safety, classifiers, runtime inspection, gateways, sandboxes, and scanners each defend a different part of the stack. This guide compares the approaches honestly - including where the tool writing this page fits and where it does not.
The landscape in mid-2026
AI agent security matured rapidly in 2025 and 2026. Multiple enterprise acquisitions consolidated the top end of the market: Lakera into Check Point, Robust Intelligence into Cisco, and Protect AI into Palo Alto Networks. Meanwhile, free tools like Meta's PromptGuard 2, Microsoft's Agent Governance Toolkit, and native OS sandboxing in coding agents expanded the baseline defenses available to everyone. The space is validated but crowded, and different tools solve genuinely different problems.
The most important distinction is not which tool is "best" but which layer of the stack each tool defends. An agent's security posture has gaps wherever a layer is missing, regardless of how strong the other layers are.
The six approach categories
1. Model-level safety
What it is: Built-in alignment training (RLHF, constitutional AI, system prompt enforcement) that shapes the model's default behavior. This is what prevents a well-trained model from following obviously harmful instructions most of the time.
What it catches: Blatant harmful requests, policy violations the model was specifically trained against, basic jailbreak attempts that the training distribution covers.
What it misses: Indirect injection from tool results (the model cannot distinguish data from instructions structurally), novel attack phrasings outside the training distribution, adversarial prompts specifically crafted to bypass alignment. Model safety is necessary but not sufficient for agents with tool access.
2. Prompt injection classifiers
What it is: Standalone models trained specifically to classify text as benign or adversarial. Meta's PromptGuard 2 (free, MIT-licensed, approximately 97.5% on its benchmark) is the most prominent example. These run as a pre-filter on inputs before they reach the agent model.
What it catches: A broad range of known injection patterns on the input they score, with high recall on established attack templates.
What it misses: Classifiers operate on individual text inputs. They do not inspect tool calls, tool results, output boundaries, or multi-step behavioral trajectories. A classifier that scores user input cannot see an injection arriving through an MCP tool result three steps later. False positives can also be an issue at high sensitivity settings, creating alert fatigue.
3. Runtime inspection layers
What it is: AI agent runtime security middleware sits inside the agent loop and inspects boundaries as the agent operates: user input, tool dispatch, tool results, and model output. Clawmont is a runtime inspection layer for OpenClaw agents. Clawkeeper (by RAD Security) provides agent governance and runtime controls across multiple agent surfaces.
What it catches: Attacks that manifest at execution time - prompt injection in tool results, dangerous tool calls, credential exfiltration, covert data channels, and multi-step escalation patterns. The multi-boundary approach catches threats that single-point classifiers structurally miss.
What it misses: Detection is still pattern-dependent. Clawmont catches the majority of known attacks on a tested corpus and far fewer of the novel, unseen ones (the exact measured rates are published on the security page). Novel natural-language attacks that avoid all known structural patterns will get through the deterministic layers. Runtime inspection also cannot substitute for source-level review of agent dependencies.
4. Enterprise API gateways
What it is: Centralized policy enforcement points that route all model and tool traffic through a single gateway. Products from Check Point (formerly Lakera), Palo Alto Networks (formerly Protect AI), and Cisco (formerly Robust Intelligence) target organizations that ship AI-powered applications to their own users.
What it catches: Organization-wide policy violations, data loss prevention rules, compliance requirements, and centralized threat intelligence. These platforms offer RBAC, SSO, audit trails, and fleet management that individual tools cannot.
What it misses: Enterprise gateways protect applications a company ships to users - a different problem from protecting a developer's own local agent. They require significant deployment overhead, a CISO buyer, and enterprise pricing. If you are an individual developer or small team running local agents, these tools are not designed for your use case.
5. OS-level sandboxing
What it is: Process-level isolation that restricts which files, network endpoints, and system calls an agent can access. Claude Code, Cursor, and OpenAI's Codex have shipped native sandboxing features for their coding agents. This is containment, not detection.
What it catches: If an agent is compromised, a sandbox limits what it can reach. File system restrictions prevent reads outside the project directory. Network restrictions prevent connections to unauthorized endpoints. This is the last line of defense when detection fails.
What it misses: Sandboxes cannot judge intent. An agent exfiltrating data through a permitted API endpoint passes the sandbox. An agent writing malicious content to an allowed file passes the sandbox. Sandboxing and detection are complementary - use both.
6. Supply chain and static scanners
What it is: Tools that analyze MCP servers, tool descriptions, dependencies, and agent configurations before the agent runs. Snyk's acquisition of Invariant Labs brought MCP-specific scanning to a major security platform.
What it catches: Known-vulnerable dependencies, malicious tool descriptions (tool poisoning), misconfigured permissions, and supply chain compromises in MCP servers. These problems exist before runtime and can be caught by static review.
What it misses: Runtime attacks. A perfectly clean, reviewed MCP server can still return poisoned data from an external source at execution time. Static analysis cannot see the attacks that arrive through live inputs.
Comparison table
| Approach | Catches | Misses | Cost |
|---|---|---|---|
| Model safety | Blatant harmful requests, known jailbreaks | Indirect injection, novel phrasings, tool-level attacks | Free (built-in) |
| Classifiers | Broad input-level injection detection | Tool results, output boundary, multi-step attacks, false positives | Free (PromptGuard 2) to paid |
| Runtime inspection | Tool-call abuse, indirect injection, credential leaks, behavioral patterns | Novel attacks beyond pattern coverage, no containment | $9.99-$19.99/mo (Clawmont); $0-$23/seat (Clawkeeper) |
| Enterprise gateways | Org-wide policy, DLP, compliance, fleet control | Not designed for individual dev/local agent use cases | Enterprise pricing |
| Sandboxing | Limits blast radius when detection fails | Cannot judge intent, passes permitted-but-malicious actions | Free (built into some agents) |
| Supply chain scanners | Known-bad dependencies, tool poisoning, misconfig | Runtime attacks, live input injection | Free to paid |
Where Clawmont fits - and where it does not
Clawmont is a runtime inspection layer for OpenClaw agents. It inspects four boundaries (input, tool dispatch, tool results, model output) locally on the user's machine, with zero false positives on the tested corpus and API keys that never leave the machine. It is licensed under BUSL-1.1 (source-available, not open source) and priced at $9.99/month bundled with a persona setup or $19.99/month standalone.
Where Clawmont is strong: local-first privacy (keys never leave), zero false positives (no alert fatigue), four-boundary depth (not just input classification), published and honest detection metrics, coverage across 8 of 10 OWASP agentic risks, affordable individual pricing with no enterprise overhead.
Where Clawmont is limited: OpenClaw-only (does not cover Cursor, Claude Code, Copilot, or other agent surfaces), pattern-based detection has a structural ceiling so it catches fewer novel attacks than known ones (measured rates are published on the security page), no OS-level sandboxing (detection, not containment), no enterprise fleet management, no free tier. If you need cross-agent coverage, Clawkeeper by RAD Security covers more surfaces. If you need raw detection recall, Meta PromptGuard 2 scores higher on its benchmark.
We publish these limitations because honest defense-in-depth is more useful than a marketing page that hides the gaps. The right question is not "which single tool should I use" but "which combination of layers covers the threats my deployment actually faces."
Choosing the right combination
Individual developer running local agents through OpenClaw: Model safety (free, built-in) plus runtime inspection (Clawmont) plus whatever OS sandboxing your environment supports. Add a static scanner if you use community MCP servers.
Developer using multiple agent surfaces: A cross-agent governance tool like Clawkeeper covers more surfaces. Layer with model safety and sandboxing.
Organization shipping AI features to users: Enterprise gateways (Check Point, Palo Alto, Cisco) are built for this. They offer the compliance, RBAC, and fleet management that individual developer tools do not. Clawmont is not designed for this use case.
Try Clawmont's runtime inspection
Test prompt injection detection, tool-call inspection, and credential leak catching in the interactive playground.
Further reading
- How to detect prompt injection attacks in AI agents
- MCP security best practices for AI agent deployments
- Runtime security vs static analysis for AI agents
- Clawmont vs Clawkeeper - an honest comparison
FAQ
What are the main categories of AI agent security tools?
The main categories are: model-level safety (built-in alignment and system prompts), prompt injection classifiers (standalone models that score inputs for adversarial content), runtime inspection layers (middleware that checks agent boundaries during execution), enterprise API gateways (centralized policy enforcement for organizations), OS-level sandboxing (process isolation that limits blast radius), and supply chain scanners (static analysis of agent dependencies and MCP servers).
Which approach to AI agent security is best?
No single approach is sufficient. Model safety cannot catch indirect injection from tool results. Classifiers operate on individual inputs and miss multi-step attacks. Runtime inspection cannot prevent attacks it has no pattern for. Sandboxing limits damage but cannot judge intent. The strongest posture layers multiple approaches at different points in the agent lifecycle.
How does Clawmont compare to free alternatives like Meta PromptGuard 2?
Meta PromptGuard 2 is a free, MIT-licensed prompt injection classifier that achieves approximately 97.5% detection on its own single-input benchmark. Clawmont takes a different approach: it covers four agent boundaries (not just input classification), reports zero false positives (no alert fatigue), runs as local-first middleware inside the agent loop, and includes tool-call inspection and credential leak detection. Clawmont publishes its own measured detection rates on the security page. They solve related but different problems and can be used together.
Is Clawmont source-available?
Yes. Clawmont is licensed under BUSL-1.1, which is source-available but not open source by the OSI definition. The license converts to Apache 2.0 in 2028. You can read the source, audit the detection logic, and verify claims against the code.
Does Clawmont work with agents outside OpenClaw?
No. Clawmont runs as a plugin inside the OpenClaw gateway and only protects agents that run through that gateway. If your agents run through Cursor, Claude Code, Copilot, or other surfaces, Clawmont does not cover them. For multi-surface coverage, look at cross-agent governance tools like Clawkeeper by RAD Security.