🔥SignSafe Part 3 — Mobile Version Demo Running on iPhone 17 in airplane mode. Model: Gemma 4 E2B GTE Analyze signing payloads locally, classify the risk, and explain what the user is about to sign. @QVAC #QVAC #AiSecLabs #BuildInPublic #Web3Security

🔥SignSafe Part 2 — Web Version Demo @qvac Built a web demo for testing malicious signing detection. The goal is simple: decode signing payloads before approval, classify the risk, and explain what the user is actually signing in plain language. Supports detection of 100 signing patterns. No blind signing. #QVAC #AiSecLabs #BuildInPublic #Web3Security

Building SignSafe「Sign心安」for the @QVAC Hackathon 🚀 A local-first edge AI tool that analyzes signing payloads before approval — helping users understand exactly what they’re about to sign. Runs on-device. No cloud. No blind trust. #QVAC #AiSecLabs #BuildInPublic

Got an idea for Web3 security but need AI credits? 🔥DeFiHackLabs AI Credits Initiative is now open. We’re supporting builders and white hats using AI to create security public goods. Apply 👇

Thank you to everyone who supported DeFiHackLabs in TheDAO’s Ethereum Security QF Round on Giveth. We received ~$2.8K in community donations and 11.033 ETH in matching support. This support helps us keep building for Ethereum security and the white-hat community.

Bastet has been accepted to @BlackHatEvents USA 2026 Arsenal! We couldn’t have reached this milestone in under 15 months without our amazing contributors and sponsors. Thank you and looking forward to sharing our work in Las Vegas. #BHUSA

Black Hat Asia 2026 was a vibe! 🦁 Spent an incredible week in Singapore presenting at the Arsenal, but the best part? The people. Nothing beats the energy of meeting new friends and catching up with the @DeFiHackLabs crew in person. (1/4) 🧵

DeFiHackLabs & Unphishable have been selected for the @thedaofund Ethereum Security Quadratic Funding round on @Giveth! 🚀 Our third project, AiSec Labs, is coming soon — and will be a key focus for us this year. 🚀 Support us and help strengthen Ethereum security 👇

Huge thanks to the for highlighting @DeFiHackLabs in the ETH Rangers recap 🙏

Here's the setup: 70 real exploits on Ethereum and BSC across 8 vulnerability categories. Ground-truth labels adapted from @DefiHackLabs' DeFi Security Breach RCA dataset. Huge credit to them for putting up the repo: github.com/SunWeb3Sec/DeFi-S… I tested 7 configurations from @OpenAI (GPT-5, GPT-5.3-Codex, GPT-5.4 High/xHigh) and @AnthropicAI (Sonnet 4.6 High, Opus 4.6 High/Max) in their native agent runtimes. 490 runs total, each graded by two independent LLM judges.

Introducing TxAnalyzer, an open-source AI-powered blockchain attack transaction analyzer Pulls traces, opcodes, and contract artifacts with one command, then automatically identifies root causes of exploits via AI Agents. Runs as a Skill — plug into Claude Code / Cursor github.com/BradMoonUESTC/TxA…

#Bastet @Kaggle Competition: Quick Reminders 📝 To keep things smooth, here’s what you need to know: 1. Leverage the Tag Definitions 2. Fewer False Positives Lead to Higher Scores 3. Review Prize Rules & Complete Pre-Registration

Honored to be recognized as one of the first 40 ETHSecurity Badge holders by @thedaofund More to build. 🙏 #EthereumSecurity #Web3Security

🚀 Building an exploit-skill with LLMs I let an LLM run for 3 days. 302 rounds later: ✅ 5,771 exploit techniques & patterns across 84 vulnerability types ✅ Obsidian KB integrated Turns out: Using Obsidian as a lightweight KB works insanely well for iterative learning loops.

AiSec (AI Security Initiative) A new initiative by @DeFiHackLabs , advancing LLM security (LLMSec) and beyond. AiSec — securing AI agents and autonomous systems, advancing LLM-driven offense and defense. We use LLMs to scale security — finding more vulnerabilities, reducing false positives, and building stronger defenses for AI-powered systems and workflows. Focus areas: ✅Smart contract security ✅AI agent & autonomous system security ✅LLM vulnerability research & detection We’re starting to share our initial work in open-source LLM security — with more tools and research coming soon. We’re building this in the open — contributors are welcome to join us.

🔥Just open-sourced: llm-sast-scanner skill. Supports 34 vulnerability categories. Your AI coding agent already knows how to find vulnerabilities. It just doesn’t know where to look, what to trace, or when to stop flagging false positives. So I gave it a structured SAST workflow: source mapping, taint tracing, and a Judge verification step. Benchmarks: F1 = 0.991 (Claude Opus 4.6) F1 = 0.954 (GPT-5.4) 👇Contributions are welcome to help improve detection rates! #Claude #Codex #Skills #SAST