Capability Based Access Control With Unsafe Decorator Gating

via “capability-based access control with @unsafe decorator gating”

AI-powered reverse engineering assistant that bridges IDA Pro with language models through MCP.

Unique: Implements decorator-based capability gating (@unsafe flags) that requires explicit opt-in from MCP clients to access privileged operations (debugging, code execution, memory writes), providing defense-in-depth against accidental or malicious privilege escalation

vs others: More explicit than implicit permission models because @unsafe decorators make privileged operations visible in code, and more flexible than role-based access control because capabilities can be enabled per-client without modifying server code

via “unsafe operations with @unsafe decorator gating (debugging and code execution)”

AI-powered reverse engineering assistant that bridges IDA Pro with language models through MCP.

Unique: Implements explicit @unsafe decorator-based gating for dangerous operations, requiring configuration opt-in to prevent accidental execution while enabling advanced users to leverage IDA's debugging and scripting capabilities

vs others: Decorator-based safety gating provides explicit opt-in for dangerous operations, preventing accidental misuse while maintaining full access for advanced users; alternative approaches (blanket disabling or no gating) either limit functionality or create security risks

via “security-first agent sandboxing with capability-based access control”

Local-first personal agentic OS and everything app for coding, knowledge work, web design, automations, and artifacts.

Unique: Implements capability-based security model where agents declare permissions upfront and runtime enforces them through policy engine with prompt injection detection and comprehensive audit logging, rather than relying on implicit trust or post-hoc monitoring

vs others: More granular than basic API key isolation and more practical than full sandboxing (containers/VMs) for local agent deployments, with explicit audit trail vs. implicit logging in most agent frameworks

via “capability-based-access-control-for-code-operations”

I made this for myself, and it seemed like it might be useful to others. I'd love some feedback, both on the threat model and the tool itself. I hope you find it useful!Backstory: I've been using many agents in parallel as I work on a somewhat ambitious financial analysis tool. I was juggl

Unique: Uses kernel-level capability-based access control (seccomp, AppArmor, SELinux) to enforce fine-grained permissions on code execution, preventing even privileged code from performing unauthorized operations — goes beyond traditional role-based access control by operating at the system call level

vs others: More secure than application-level access control because code cannot bypass kernel-level enforcement; more flexible than static allowlists because capabilities can be dynamically configured based on code requirements

via “resource-access-control-with-capability-binding”

AgenShield — AI Agent Security Platform

Unique: Uses capability-based security model where agents receive explicit grants of allowed tools rather than checking permissions at invocation time, enabling efficient enforcement and clear visibility into agent capabilities. Supports context-aware binding where capabilities can vary based on tenant, user, or execution context.

vs others: Implements capability-based security (explicit grants) rather than permission-based (implicit allows), providing stronger isolation guarantees and clearer audit trails