PromptSonar: A Static Security Scanning Tool for AI Prompts and Agent Workflows

This article introduces PromptSonar, a zero-LLM-dependency, local-first static security scanner designed for AI prompts, MCP configurations, and agent workflows, fully aligned with the OWASP LLM Top10 security standards. Original author/maintainer: meghal86; Source platform: GitHub; Original link: https://github.com/meghal86/promptsonar; Source publication/update time: 2026-06-04T21:44:31Z. This tool aims to address new security risks brought by the rapid popularization of AI systems, providing a lightweight and low-cost security detection solution.

As LLM and agent technologies are rapidly integrated into various applications, new security risks such as prompt injection attacks, sensitive information leakage, and malicious workflow execution are becoming increasingly prominent, which traditional security scanning tools are difficult to address. The OWASP LLM Top10 security list released in 2023 outlines the main threats, but how to translate these into actionable detection capabilities remains a challenge for the industry.

PromptSonar is positioned as a "zero LLM call, local-first" static security scanning tool that does not rely on external large model services, with all analyses completed locally. Its core detection objectives include three categories: 1. AI prompts (detecting injection vulnerabilities, sensitive information leakage, etc.); 2. MCP configurations (scanning for security risks); 3. Agent workflows (analyzing potential risk points).

The design philosophy of PromptSonar includes: 1. Zero LLM Calls: Pure static analysis with no API calls, advantages include controllable cost, privacy protection, fast response, and reliability; 2. Local First: Suitable for offline development, enterprise scenarios with strict data control, CI/CD pipelines, and rapid development feedback; 3. Alignment with OWASP LLM Top10: Covers rules such as LLM01 (Prompt Injection), LLM02 (Insecure Output Handling), LLM06 (Sensitive Information Leakage), LLM07 (Insecure Plugin Design), etc.

PromptSonar uses static analysis technology to identify risks through pattern matching, semantic analysis, and rule engines, with obvious advantages in speed and scalability. Typical use cases: Real-time detection during development, code review assistance, CI/CD integration for automatic scanning, and batch security audits of existing AI applications.

PromptSonar addresses the pain point of managing security risks in AI application development, enabling the "shift-left security" concept to be implemented in the AI field. For development teams: Capture issues early and reduce repair costs; For security teams: Provide automated, integrable tools to establish AI application security baselines.

PromptSonar represents an important direction for AI security tools—establishing a security protection system that does not rely on LLMs, balancing cost, data sovereignty, and stability. As the scale of AI applications expands, such specialized tools will work with model-layer security mechanisms and runtime protection to form a multi-layered defense system, safeguarding the healthy development of AI.