Agentic Security provides a comprehensive red-teaming toolkit specifically designed for LLM-powered agent workflows. As AI agents gain access to tools, APIs, and sensitive data, the attack surface expands well beyond simple prompt injection. This scanner tests the full range of vulnerabilities including multi-step jailbreak chains, multimodal attacks across text, image, and audio inputs, and randomized fuzzing that uncovers unexpected edge-case behaviors in production models.
The toolkit connects directly to any LLM API endpoint and runs high-volume attack scenarios drawn from a growing dataset of adversarial prompts. Its reinforcement learning module crafts adaptive probes that evolve based on the model's responses, simulating sophisticated attackers who adjust their strategy in real time. Each scan generates detailed reports identifying which attack vectors succeeded, the severity of each vulnerability, and recommended mitigations.
For security teams and ML engineers shipping agent-based products, Agentic Security fills a critical gap in the testing pipeline. Traditional software security tools cannot evaluate the probabilistic and context-dependent nature of LLM outputs. By integrating this scanner into CI/CD workflows or running it as a standalone audit, teams can systematically validate that safety guardrails hold up against the latest attack techniques before exposing their agents to real users.
