QSAG-Core vs Shannon

QSAG-Core is a Python security scanner specifically designed for the OWASP Top 10 for Agentic Applications 2026 threat model. It provides three core detection capabilities: MCP tool poisoning (26 malicious patterns across 7 categories), prompt injection (28+ attack patterns including goal hijacking, jailbreak attempts, and memory poisoning), and ghost agent detection for unauthorized API key usage. It runs as pure pattern matching — no ML, no cloud dependency — and can be integrated as a pre-execution guard in any Python-based agent pipeline. Released April 10, 2026 by the Neoxyber team, QSAG-Core fills a real operational gap as MCP-based agent deployments proliferate. While Microsoft's Agent Governance Toolkit addresses similar territory, it's heavyweight and enterprise-focused. QSAG-Core is a pip install and a few lines of code — the security-focused indie alternative that fits into a CI/CD pipeline or an existing agent framework without an enterprise contract. The threat model it addresses is timely. As MCP becomes the de facto standard for tool-calling in AI agents, malicious MCP servers and prompt injection via tool outputs are becoming documented attack vectors. Having a lightweight, open-source scanner that specifically targets these patterns is exactly what the community has been building toward. MIT licensed, 24 commits in its first day.

Shannon is an autonomous AI security testing agent that does what most scanners can't: it actually proves vulnerabilities are real before reporting them. Built by Keygraph, it analyzes your source code and API endpoints, identifies attack surfaces, and then autonomously executes live exploits — SQL injection, XSS, SSRF, authentication bypasses, and more. The key differentiator is evidence-first reporting: Shannon won't flag a potential SQL injection unless it can demonstrate the exploit working in your environment. Under the hood, Shannon uses Claude to reason about code structure and attack chains, combining static analysis with dynamic exploitation in a feedback loop. It maps the application graph, selects attack strategies based on code patterns, attempts the exploit, and reports only confirmed vulnerabilities with full reproduction steps. It runs locally and can be pointed at any web app or API. The timing is pointed: AI coding assistants are shipping code faster than teams can review it for security. Shannon was born from that gap — an AI to check the work of other AIs. At ~$40-55 in API credits per full scan, it's priced for startups who can't afford a dedicated security team but can't afford a breach either. The AGPL open-source release makes it accessible to indie developers and security researchers.