AI tool comparison
AutoProber vs Shannon
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Security
AutoProber
AI-driven hardware hacking arm — CNC-controlled PCB probing with an LLM agent
50%
Panel ship
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Community
Paid
Entry
AutoProber is an open-source hardware security research platform that puts an LLM agent in control of a physical CNC machine to autonomously probe circuit boards. The build uses off-the-shelf parts: a webcam, a USB microscope, a cheap CNC frame, and a probe tip. The agent handles the full hacking workflow — target PCB discovery, microscope-assisted mapping of test points, CNC motion planning with safety bounds checking, and controlled pin probing for UART/JTAG/SWD interfaces. The software stack is pure Python. The agent generates motion commands in a DSL, validates them against hardware safety constraints before execution, and updates an exploration map as it discovers new test points. GainSec posted a demo video showing the arm autonomously locating and probing a router PCB's debug interface without human intervention after initial setup. What makes this genuinely novel isn't the individual components — hobbyists have built CNC probers before — but the LLM-in-the-loop architecture that turns the whole process from a manual expert skill into a semi-automated one. Security researchers who previously needed 15 years of experience to read a PCB layout now have a tutor and co-pilot on the physical bench.
AI Security
Shannon
Autonomous AI pentester that proves exploits, not just finds them
75%
Panel ship
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Community
Paid
Entry
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.
Reviewer scorecard
“The safety constraint validation layer before any CNC motion is the right call and shows the author understands what goes wrong when you mix LLMs with physical actuators. The DSL for motion commands is clean. This is a real research tool, not a toy.”
“This solves a real problem I face constantly: AI-generated code shipping faster than security reviews can keep up. Shannon catches what static linters miss because it actually runs the exploit — that's a fundamentally different class of tool. At ~$50 per scan it's cheaper than one hour of a security consultant's time.”
“The agent hallucinates PCB pin assignments in about 20% of cases based on the demo, which in a physical system means a bent probe or a shorted component. The hardware cost to build a reliable version is non-trivial, and you still need domain expertise to validate what the agent decides.”
“Every 'autonomous pentester' of the past decade has promised to replace human red teamers and delivered glorified CVE scanners. The AGPL license is also a poison pill for enterprise teams who need commercial contracts before running anything against production. Wait for a version with a proper SaaS tier and audit trail.”
“This is physical AI applied to the supply chain security problem. AI-assisted hardware auditing could eventually make it practical to spot tampered firmware chips or backdoored components at scale — a national security capability currently gated behind a tiny pool of expert humans.”
“We're entering an era where AI writes code and AI breaks code — Shannon is the first credible entry in the adversarial AI category for developers. The agentic loop of analyze-exploit-verify is the right architecture. This becomes infrastructure-grade once it integrates into CI/CD pipelines as a mandatory gate.”
“Not my domain, but the demo video is one of the coolest things I've seen this week. The moment the arm autonomously repositions based on the microscope view is genuinely impressive. Niche hardware security tool, but an inspiring proof of concept for physical AI.”
“As someone who builds web tools and can't afford a dedicated security team, Shannon feels like a genuine safety net. The output is human-readable with full reproduction steps — not a wall of CVE numbers I have to decode. Exactly what indie builders need.”
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