AgentWard
Secure every agent action — from install to runtime.
Source-available security platform for AI agents.
Telling an agent "don't touch the stove" is a natural-language guardrail that can be circumvented. AgentWard puts a physical lock on the stove — code-level enforcement that prompt injection can't override.
AgentWard sits between AI agents and their tools (MCP servers, HTTP gateways, function calls) to enforce least-privilege policies, inspect data flows at runtime, and generate compliance audit trails. Policies are enforced in code, outside the LLM context window — the model never sees them, can't override them, can't be tricked into ignoring them.
Why AgentWard?
AI agents now have access to your email, calendar, filesystem, shell, databases, and APIs. The tools exist to give agents these capabilities. But nothing exists to control what they do with them.
| What exists today | What it does | What it doesn't do |
|---|---|---|
| Static scanners (mcp-scan, Cisco Skill Scanner) | Scan tool definitions, report risks | No runtime enforcement. Scan and walk away. |
| Package scanners (Snyk, Socket) | Flag known-vulnerable packages | Don't inspect .pth files or install-time code execution vectors. |
| Guardrails frameworks (NeMo, Guardrails AI) | Filter LLM inputs/outputs | Don't touch tool calls. An agent can still rm -rf /. |
| Prompt-based rules (SecureClaw) | Inject safety instructions into agent context | Vulnerable to prompt injection. The LLM can be tricked into ignoring them. |
| IAM / OAuth | Control who can access what | Control humans, not agents. An agent with your OAuth token has your full permissions. |
