Alternatives
Products that do what EAIVP — Aletheia Prism does
Forensic malware analysis that explains itself
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- 3MS
I noticed the growing security concerns around MCP (https://news.ycombinator.com/item?id=43600192) and built an open source tool that can detect several patterns of tool poisoning attacks, exfiltration channels and cross-origin manipulations. MCP-Shield scans your installed servers (Cursor, Claude Desktop, etc.) and shows what each tool is trying to do at the instruction level, beyond just the API surface. It catches hidden instructions that try to read sensitive files, shadow other tools' behavior, or exfiltrate data. Example of what it detects: - Hidden instructions…
2025 · github.com
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Aegisora▲93The narrow control plane for AI agent tool and API calls.
Aug 2026 · aegisora-ai.vercel.app
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This is a simple single-file python program that can find basic XSS (cross-site scripting) vulnerabilities in a target url. Most XSS discovery tools use a payload refelection strategy in which payloads are injected in url parameters and the GET response is inspected for places where the payload content is reflected. This is a very low precision XSS detection strategy because most reflection does not support execution. This program uses a different approach, and instead opens the target url in a browser, tests alert(...) payloads directly in the browser context, and listens for an alert being…
2024 · github.com
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One click to know if an image is AI-generated or real
Jun 2026 · chromewebstore.google.com
- 10IB
I built a GitHub app that detects it in pull requests, notifies or blocks them. Alongside it, I published a Semgrep ruleset for any stage of the CI/CD. I started this after getting frustrated by all the FUD around malicious code - lots of noise, little effort to solve it. Having said that, it's still a major attack vector - a stored RCE, with the codebase itself as the sink. Feedback is appreciated. The app, PRevent - https://github.com/apiiro/PRevent The ruleset: https://github.com/apiiro/malicious-code-ruleset The research:…
2025 · github.com
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AI that finds discrepancies between narratives and evidence.
Feb 2026 · justitia-lens.netlify.app
- 14DI
I built an open-source malware detection daemon that monitors all running processes in real-time using ML + heuristics. No kernel modules or eBPF required. Key points: - Polls /proc for new processes (works on any Linux kernel 2.6+) - Random Forest model trained on EMBER 2018 dataset (2.3M samples) - Heuristic rules for crypto miners, ransomware, rootkits - ~20MB RAM, <1% CPU, sub-millisecond scan latency - Pure C, zero runtime dependencies - Model embedded directly in binary (50KB) Why I built this: Existing solutions either require modern kernels (eBPF) or are heavy/proprietary.…
Nov 2025 · github.com
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Heya HN, excited to show off what I've been privately calling an AI cybersecurity tool built by AI skeptics. Two years ago we started a series of experiments with this philosophy of identifying small pieces of cognitive work where a human can very clearly map out the input data they need and the algorithm they'd follow to make a decision. This idea came partly out of frustration with the zeitgeist involving throwing broad AI features (e.g. useless chat bots) into products that end up unreliable and are targeting no clear problem a user might actually have. It feels kind of like a machete vs.…
2025 · semgrep.dev
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Jul 2026 · meltedinhex.com
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We originally set out to solve complex debugging headaches and useless alerts caused by traditional security scanners in our own projects. Static Analysis (SAST) flagged too much noise because it couldn't verify runtime context, while Dynamic Analysis (DAST) missed internal logic bugs because it treated the app like a black box. We built a CLI tool to bridge this gap using grey box testing from a red team approach. We use internal knowledge of the codebase to guide parallel execution, allowing us to find complex or hidden logic errors and attack paths standard linters/scanners miss. The…
Dec 2025 · bloodhoundsecurity.ca
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