Silmaril offers a security firewall that wraps AI inference calls to block harmful outcomes in real time.
Funding
Funding not disclosed
Founders
Product
Problem
AI-powered applications increasingly expose attack surfaces through public inputs, trusted execution contexts, and open web integrations, allowing adversaries to perform prompt injection, tool abuse, and context poisoning that can lead to data leakage, financial fraud, and service disruption.
Solution
Silmaril provides a security firewall that intercepts AI inference calls to prevent harmful outcomes before they occur. Autonomous agents continuously probe the application’s UI and APIs to discover and map trust boundaries, chaining multiple attack techniques into realistic exploits. A low‑latency classifier, trained on the application’s execution traces, evaluates user intent, tool calls, and accumulated state in real time, blocking risky interactions. Each detected attack is transformed into synthetic training data, enabling the defense model to be updated and redeployed within an hour and shared anonymously across deployments. This approach combines proactive vulnerability discovery with real‑time protection and rapid retraining to safeguard AI services.
Target Audience
Primary customers are developers and security teams building AI agents, LLM‑driven applications, and enterprise AI services that need to protect against prompt injection, tool abuse, and context‑based attacks.
Features
- Autonomous probing agents that simulate prompt injection, tool misuse, and context poisoning to uncover vulnerabilities before attackers exploit them
- Real‑time firewall classifier that ingests intent, application context, and execution state as a unified signal to block malicious user inputs and tool calls
- Synthetic attack data generation pipeline that creates training examples from discovered exploits for immediate model retraining
- Sub‑hour defense update cycle with anonymized sharing of protections across multiple deployments
- Compatibility layer that wraps existing inference APIs, requiring no changes to the underlying AI model or infrastructure