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I built a local fuzzing tool to red-team LLM agents (Python, SQLite)

Hacker News

I built a local fuzzing tool to red-team LLM agents (Python, SQLite)

I spent the last week building a local-first security tool because I was tired of paying $500/mo for enterprise SaaS just to test my AI agents for basic vulnerabilities. The tool is called Agent Exam Pro. It's a Python-based fuzzer that runs locally on your machine (no cloud data leaks). How it works: The Engine: Takes a base test case and runs it through 16 mutation strategies (Base64, Roleplay, Token Smuggling) to generate 1,000+ variations. The Payloads: I curated 280+ real-world exploits from open-source lists (PayloadBox, PayloadsAllTheThings) to test for SQLi and XSS in agent tool calls. The Judge: Uses a local LLM (via Ollama) or OpenAI to grade responses on safety rather than just regex matching. The Audit: Logs everything to a local SQLite database. I'm selling the source code as a one-time purchase (no subscriptions) because I prefer owning my tools. You can check it out here: https://woozymint.gumroad.com/l/agent-exam-pro

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: llama, 000, io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas, subscription · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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