Shannon is a fully autonomous AI pentester designed to discover and exploit vulnerabilities in web applications, providing concrete proof of exploitability rather than mere alerts. It aims to bridge the security gap created by rapid code deployment and infrequent manual penetration tests by acting as an on-demand, white-box pentester that performs continuous security validation.

Key features include fully autonomous operation, requiring only a single command to initiate a pentest, including handling advanced 2FA/TOTP logins and browser navigation. It generates pentester-grade reports with reproducible Proof-of-Concepts (PoCs) to eliminate false positives. Shannon currently identifies and validates critical OWASP vulnerabilities such as Injection, Cross-Site Scripting (XSS), Server-Side Request Forgery (SSRF), and Broken Authentication/Authorization. Its core strength lies in its code-aware dynamic testing, where it analyzes source code to intelligently guide its attack strategy, then performs live, browser- and command-line-based exploits on the running application. It integrates leading reconnaissance and testing tools like Nmap, Subfinder, WhatWeb, and Schemathesis for deep environmental analysis and utilizes parallel processing to accelerate analysis and exploitation phases.

The system is offered in two editions: Shannon Lite (AGPL-3.0), which focuses on the core autonomous AI pentesting framework and is intended for security teams and researchers, and Shannon Pro (Commercial), designed for enterprises, offering advanced features, CI/CD integration, dedicated support, and an LLM-powered data flow analysis engine for deeper vulnerability detection. Both versions are white-box tools, requiring access to the application's source code and repository.

Shannon's core methodology emulates a human penetration tester through a sophisticated multi-agent architecture built around Anthropic's Claude Agent SDK as its reasoning engine. This architecture combines white-box source code analysis with black-box dynamic exploitation, managed by an orchestrator across four distinct phases:

1. Reconnaissance: This initial phase constructs a comprehensive map of the application's attack surface. Shannon analyzes the provided source code and integrates with external reconnaissance tools (e.g., Nmap for port scanning and service detection, Subfinder for subdomain enumeration, WhatWeb for web technology identification) to understand the target's tech stack and infrastructure. Concurrently, it performs live application exploration via browser automation to correlate code-level insights with real-world behavior, yielding a detailed map of all entry points, API endpoints, and authentication mechanisms for subsequent phases.

2. Vulnerability Analysis: Operating in parallel for efficiency, this phase utilizes the reconnaissance data. Specialized agents, each dedicated to a specific OWASP category (e.g., Injection, SSRF), hunt for potential flaws. For vulnerabilities like injection, agents execute structured data flow analysis, meticulously tracing user input from source to dangerous sinks within the application's code. The output of this phase is a list of hypothesized exploitable paths.

3. Exploitation: Continuing the parallel workflow to maintain speed, this phase is dedicated to validating the hypotheses from the previous stage. Dedicated exploit agents receive the hypothesized paths and attempt to execute real-world attacks. This involves browser automation, command-line tool execution, and custom scripting to demonstrate impact. A strict "No Exploit, No Report" policy is enforced: if a hypothesis cannot be successfully exploited to prove vulnerability, it is discarded as a false positive, ensuring only actionable findings are reported.

4. Reporting: The final phase compiles all validated findings into a professional, actionable report. An agent consolidates the reconnaissance data and all successful exploit evidence, removing any noise or potential LLM hallucinations. Only verified vulnerabilities are included, complete with reproducible, copy-and-paste Proof-of-Concepts, delivering a pentest-grade report focused exclusively on proven risks.

Shannon explicitly states disclaimers regarding its use: it is not a passive scanner and can have mutative effects on the target environment due to active exploitation. Therefore, it must *not* be run on production environments and is intended exclusively for sandboxed, staging, or local development setups. Users are responsible for obtaining explicit, written authorization before running Shannon on any system. The system also acknowledges LLM and automation caveats, stating that human oversight is essential to validate findings and that Shannon Lite's analysis may not be exhaustive due to LLM context window limitations, unlike Shannon Pro's advanced analysis engine. The tool specifically targets a defined set of vulnerabilities and does not cover issues it cannot actively exploit, such as vulnerable third-party libraries or insecure configurations, which are a focus of Shannon Pro.