🚨 Hackers can now hijack AI coding agents with fake Sentry errors. No phishing. No malware. No server break-in. Agentjacking tricks tools like #ClaudeCode and Cursor into reading planted error reports as trusted fix steps, then running attacker code with developer privileges. Researchers tested it on 100 organizations. Success rate: 85%. Read: thehackernews.com/2026/06/ag…

JUST IN: Microsoft has reportedly restricted employee use of Claude Fable 5 over concerns that confidential data could be retained by Anthropic.

Huge newss! One of the biggest agentic growing frameworks in github is integrated with @ATBASHai

BREAKING.: Biggest privacy token $ZEC crashed over -50% in the last 24 hours and wiped out $5 Billion from its market cap. The flaw was hidden inside Zcash's Orchard privacy pool since May 2022 and remained undetected for nearly 4 years despite multiple security audits. Security researcher Taylor Hornby reportedly used Claude Opus 4.8 AI model to build a working proof-of-concept that successfully generated counterfeit ZEC in local testing on May 29. Although the bug has now been patched on June 2, The issue is that Zcash's privacy design makes it impossible to know if any fake ZEC was minted before the fix. Unlike Bitcoin, where anyone can verify the supply, Zcash's privacy design makes it impossible to audit whether fake coins were secretly minted before the fix. The team denies any fake ZEC was minted, but traders are selling on the fear alone. Imagine someone secretly adding extra chips to a casino, but because of the way the system works, neither the casino nor the players could tell which chips were real and which were fake. Shielded Labs is exploring a proposed Network Upgrade to allow anyone to verify the integrity of Zcash supply.

Ever wondered who’s building the final safety layer for AI agents? Meet $ATBASH, the last authority before any irreversible action executes. It allows, holds, or blocks dangerous moves in real time. Just real control. 🛡️ While everyone rushes to make agents more powerful, $ATBASH focuses on one critical question: Who stops the agent when it’s about to cross the red line? Most guardrails sit inside the model. ATBASH sits outside — as an independent runtime authority. The Founders are: Yosef Soso (@0x50so) Or Perelman (@perelmanor). Two experienced builders who saw the gap early: models will get smarter, agents will get more autonomous… but without hard boundaries at execution time, we’re flying without a parachute. Yosef Soso has already interacted with the $ATBASH ticker and it looks like he has endorsed the token made for his project. They’ve been out there talking to legends like @Scobleizer about enterprise guardrails, compliance, and why “trust the model” is no longer enough. Their mission: Give developers and organizations Action Boundary Control, the ability to define and enforce what an agent is never allowed to do, no matter how convincing it sounds. Why it matters: Agents can hallucinate their way into compliance disasters, guardrails can be stripped in minutes, memory files and prompt engineering aren’t enough. ATBASH adds a separate, hardened decision layer right before execution. Interestingly, Roger Lim (from NGC Ventures, a ~$500M fund) bought $ATBASH even though his wallet had done no transactions for the last 100 days. A 500M fund definitely isn’t gambling project for no reason. If you’re building agents (especially enterprise or high-stakes ones), this is infrastructure you’ll want to know about. The future of AI isn’t just “more capable agents.” It’s capable agents that can’t break the rules, even if they really want to. Welcome to the age of enforceable boundaries. @ATBASHai coded for 100m soon, just matter of time.

⚠️ New ChatGPT Vulnerability Lets Attackers Turn Web Pages Into Phishing Payloads Source: cybersecuritynews.com/chatgp… A browser-based prompt injection technique that transforms any web page into a phishing delivery surface by exploiting ChatGPT’s page summarization feature, rendering attacker-controlled links, fake security alerts, and QR codes directly inside the trusted ChatGPT interface. The attack builds on the same trust-transfer logic previously demonstrated against Microsoft Copilot, where attacker-crafted email content could manipulate AI-generated summaries through Cross Prompt Injection Attacks (XPIA). ChatGPhish escalates that premise by swapping the bounded email primitive for the browser where users spend the majority of their working day. #cybersecuritynews #vulnerability

Important post for entrepreneurs from @a16z yesterday and a look at a new system that ensures AI agentic systems don't hallucinate their way into compliance hell. "The value comes less from the underlying model’s raw capability (though that’s still important!) than from the scaffolding around it that makes the output trustworthy, compliant, and operational inside a specific industry." Here @ATBASHai founders @0x50so Yosef Soso and @perelmanor Or Perelman talk to me about how its tool helps AI enterprise developers put guardrails around agentic systems to make sure they don't cross "red lines" that would trigger complaince problems.

Great to see our founders spend time with the legend himself. Very interesting read about what we are building here at Atbash.

Important post for entrepreneurs from @a16z yesterday and a look at a new system that ensures AI agentic systems don't hallucinate their way into compliance hell. "The value comes less from the underlying model’s raw capability (though that’s still important!) than from the scaffolding around it that makes the output trustworthy, compliant, and operational inside a specific industry." Here @ATBASHai founders @0x50so Yosef Soso and @perelmanor Or Perelman talk to me about how its tool helps AI enterprise developers put guardrails around agentic systems to make sure they don't cross "red lines" that would trigger complaince problems.