AgentVault: AI Agent Runtime Security Gateway

AgentVault is an open-source runtime security gateway designed specifically for AI Agents, aiming to build a security defense line for Agent systems. Its core positioning is a security barrier between user input and Agent execution. It addresses the unique security threats faced by AI Agents through four core functions: prompt injection detection, tool usage policy enforcement, high-risk operation interception, and audit tracking. The gateway supports deployment modes like transparent proxy, allowing integration without modifying the core code of the Agent.

AI Agents driven by large language models are moving from experiments to production, but their security architecture lags behind. Compared to traditional software, Agents face unique threats: 1. Prompt injection attacks (inducing the model to perform unintended operations); 2. Tool abuse risks (external tools becoming attack springboards); 3. Blurred permission boundaries (autonomous decision-making renders traditional access control ineffective); 4. Unpredictable behavior (uncertainty in model output); 5. Difficult audit and traceability (difficulty in fully recording multi-round interactions). These risks are particularly prominent when Agents gain system permissions or access sensitive data.

AgentVault is an open-source runtime security gateway. Its core functional modules include: prompt injection detection, tool usage policy, high-risk operation interception, and audit tracking records. Deployment modes are flexible: 1. Proxy mode (independent service, suitable for hardening existing systems); 2. Sidecar mode (deployed on the same node, low latency, suitable for K8s); 3. SDK mode (library integration, suitable for deep customization). The gateway adopts a highly available architecture to avoid single points of failure.

Prompt Injection Detection: Multi-layer strategy (pattern matching layer, semantic analysis layer, context isolation layer, behavior monitoring layer), with configurable sensitivity and rules. Tool Usage Policy: Fine-grained control (tool whitelist, parameter verification, frequency limitation, context-aware authorization), supporting multi-dimensional policy management. High-risk Operation Interception: Identifies risks such as sensitive data access, system-level operations, external communication, and resource consumption. Interception modes can be selected as block/alarm/manual review. Audit Tracking: Full-link recording (request records, reasoning tracking, tool call logs, security event markers), supporting export to SIEM systems.

AgentVault is suitable for various scenarios: 1. Enterprise knowledge base Q&A Agent (prevent unauthorized document access); 2. Code generation Agent (restrict compilation/execution tool calls); 3. Data analysis Agent (audit data access compliance); 4. Customer service Agent (isolate from core business systems); 5. Multi-Agent collaboration system (secure communication between Agents).

Comparison with Other Solutions: Compared to WAF/API gateways, AgentVault's advantages lie in understanding Agent semantics, perceiving workflows, dynamic policies, and low-intrusion integration. Security Governance Suggestions: Follow the principle of least privilege, implement input validation (defense in depth), review sensitive information in outputs, conduct regular red team tests, and formulate emergency response plans.

AgentVault is an important exploration in the field of AI security, filling the gap in security protection for Agent architectures. As Agents gain more system permissions and data access capabilities, runtime security gateways will become essential infrastructure. It is recommended that teams include security assessments in the early stages of architecture design. AgentVault's open-source implementation provides a security baseline for the community, helping to raise the security level of the ecosystem.