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4 stories in the last 7d

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1Password's AI patching benchmark is misleading

Trail of Bits reanalysis says 1Password's 26% AI clean-fix rate is misleading; 86% of eligible patches blocked exploits.

Trail of Bits critiques 1Password's FLAWED AI patching benchmark, arguing its 26% clean-fix headline mixes trials where agents were instructed to apply wrong fixes (22% of data) with trials that prohibited compiling or testing (36%). Restricting to reasonable conditions, 2,634 of 3,067 patches (86%) blocked the supplied exploit. Trail of Bits also reports 12.5% of 2,265 developer first fixes failed in its own 2024-2026 assessments, and released post-patch-validation and review-walkthrough agent skills.

Lobsters · security · 23h agoResearch

Google Chrome 153 Released With Fixes for 42 Security Vulnerabilities

Google shipped Chrome 153 to the Stable channel fixing 42 vulnerabilities, including three critical flaws in WebGL, Internals, and Workers; no exploitation reported.

Google released Chrome 153 (153.0.8010.47/48) for Windows, macOS, and Linux, patching 42 security vulnerabilities including three rated critical: CVE-2026-91726 (out-of-bounds read in WebGL), CVE-2026-91721 (use-after-free in Internals), and CVE-2026-91749 (use-after-free in Workers). Twenty-eight fixes are rated high severity, covering use-after-free, type confusion, race condition, integer overflow, and authorization flaws across components like V8, Skia, DOM, ServiceWorker, PDF, and Extensions. Google's bulletin indicates no vulnerabilities are currently being exploited in the wild, and external researchers earned rewards up to $1,500 for reported issues. Enterprises are advised to verify fleet-wide deployment via browser-management consoles and enable automatic updates.

GBHackers · 14h agoVulnerabilityCVE-2026-91726CVE-2026-91721CVE-2026-91749+27 CVEs

IntentFuzz: A Protocol-Aware Fuzzer for Automated Invariant Violation Detection in Intent-Based Cross-Chain Bridges

IntentFuzz protocol-aware fuzzer recovers bridge structure from unannotated Solidity and confirmed 22 invariant violations across 24 real-world deployments.

IntentFuzz formalizes a taxonomy separating invariant violations from settlement exposures in intent-based cross-chain bridges, then recovers a bridge's intent structure and deposit/fill function roles from unannotated Solidity source. It classified deposit and fill functions with 100% recall and 82% combined precision, and achieved 100% recall and precision on 23 planted-bug mutants. Across 24 real-world deployments it confirmed 17 genuine invariant violations with heuristic-only input generation, rising to 22 with its LLM-assisted tier, spanning eight vulnerable GitHub repositories with findings reproducible against public deployed bytecode.

arXiv cs.CR · 5d agoResearch1

From Specs to Apps: Verifying and Monitoring Models of Signal and WhatsApp

Researchers use the SpecMon runtime monitor to verify WhatsApp Web and Signal Desktop against formal Signal protocol models, finding undocumented libsignal fork differences.

The paper applies SpecMon, a runtime monitoring tool, to check whether executions of WhatsApp Web and Signal Desktop conform to formal models of the Signal protocol. The authors instrument both applications and build Tamarin-compatible multiset-rewrite models, including the first model of WhatsApp Web's implementation and the most detailed model to date of Signal's original protocol. They verify authentication and secrecy properties for core Signal protocol components, show monitoring detects deliberately injected faults with low overhead, and identify previously undocumented behavioral differences between the original libsignal library and WhatsApp's fork.

arXiv cs.CR · 6d agoResearch