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 research agent from Keygraph that takes a target (web app, API, or codebase) and runs a full offensive security workflow: static analysis, attack surface mapping across OWASP Top 10, and then actual proof-of-concept exploit execution — all without manual intervention. It orchestrates real security tools (Nmap, Subfinder, SQLMap, Playwright) under the hood, not just generating reports. The Lite tier (AGPL-3.0) handles web apps and API endpoints, running browser automation and fuzzing attacks autonomously. Shannon Pro (commercial) adds SAST/SCA integration, CI/CD pipeline hooks for PR scanning, and team-level finding management. The model layer is pluggable — defaults to GPT-4o for planning with Claude Sonnet for exploit reasoning, but can be pointed at local models. What sets Shannon apart from tools like Burp Suite or ZAP is the agentic loop: it doesn't just surface a list of potential issues, it attempts exploitation and logs what worked. For small security teams and solo founders doing pre-launch security checks, this compresses days of pentesting work into a single automated run. The open-source Lite tier is the real news here — genuine autonomous exploitation capability, freely available.