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3 stories in the last 7d

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Scaling Verification of Cryptographic Software with Aeneas, Rust, and Lean

Microsoft SymCrypt implementations of SHA-3 and ML-KEM verified in Lean via Aeneas-extracted Rust models, with AI agents writing proofs.

The paper develops a methodology for verifying production Rust cryptographic code by using Aeneas to extract pure models into Lean, avoiding low-level pointer and aliasing reasoning. Applied to Microsoft's SymCrypt, it verifies SHA-3 and ML-KEM implementations ported from C to Rust and extends SymCrypt with FrodoKEM, ML-DSA, and HPKE. A 237 KLOC Lean development establishes safety, panic-freedom, and functional correctness of 16.7 KLOC of Rust supporting post-quantum cipher suites on x86-64 and ARM. AI agents autonomously write formal proofs verified by the Lean kernel, and evaluation shows verified Rust meets SymCrypt's performance and portability requirements.

arXiv cs.CR · 2d agoResearch1

Credential Theft: How Attackers Steal & Use Stolen Credentials

Huntress explains how attackers steal credentials through phishing, AitM, infostealers, and dumping, then use them for lateral movement, BEC, and ransomware.

Huntress published an educational overview of credential theft, citing that roughly 70% of confirmed data breaches begin with stolen credentials. It details acquisition methods including phishing, adversary-in-the-middle attacks that capture MFA session tokens, infostealers (nearly a quarter of threats Huntress observed in 2025), Mimikatz-based credential dumping, credential stuffing, and password spraying. The piece then covers post-theft actions such as lateral movement, privilege escalation, account takeover, business email compromise, and ransomware, and closes with behavioral detection guidance and layered prevention strategies.

Huntress · 6d agoResearch

When the Whole Company Adopts AI: What It Does to Your SOC

Analysis of 16.9 million SOC alerts finds AI-related alerts at 0.43%, growing 685% since February, with 94.1% noise and 0.02% real attacks.

A review of roughly 16.9 million SOC alerts found about 73,000 (0.43%) were AI-related, a share that grew 685% between February and June 2026. Of AI-related alerts, 94.1% were noise, 5.8% genuine risks, and 0.02% real attacks; 79.8% received benign verdicts, 81.7% were automatically suppressed, and only 5.4% reached a human analyst. The only confirmed attacks were phishing campaigns that weaponized AI brand names as lures, while developer coding agents spawning shells and reading credential stores routinely tripped detections written before AI agents existed.

The Hacker News · 5d agoResearch2