Vardhan0257/zt-agentshield

ZT-AgentShield addresses a critical bottleneck in the 'Agentic AI' era: the security of execution boundaries when an LLM calls a tool. While the problem space is high-value, the project currently lacks any defensibility. It is a 1-day-old research artifact with 0 stars and 0 forks, functioning more as a code-drop than a community-driven tool. The moat is non-existent; the approach of 'Zero Trust' applied to agent execution is a logical extension of existing security paradigms rather than a breakthrough technical innovation. More importantly, frontier labs (OpenAI, Anthropic, Google) are essentially forced to build these capabilities natively. For example, Anthropic's 'Computer Use' and OpenAI's 'Code Interpreter' already implement proprietary sandboxing. Third-party security layers like this face a 'feature vs. product' risk where the platform provider will always have a lower-latency, more integrated version of the same safety boundary. Competitors in the startup space like Lakera, Giskard, and Menlo Security are already moving toward execution-layer defense with significantly more capital and established integration points.