Search: “sms”

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DomainAll Cyber AIWindow24h 3d 7d 30d AllIn the wild Important only Show duplicates

CategoryAll Malware 850 Phishing & fraud 481 Vulnerability 258 Ransomware 248 Data breach 230 Threat actor 140 Exploit / PoC 112 Policy & legal 93 Industry 70 AI tools & infra 20 Research 19 Advisory 14 Tools 6 AI safety & security 4

SeverityAll critical 573 high 773 medium 917 low 243 info 43

ReCAST: Restoration-aware Cascaded Stage-wise Training for Obfuscated SMS Risk Classification

ReCAST distills a large teacher model's de-obfuscation ability into smaller models for robust classification of obfuscated Chinese SMS fraud messages.

The paper proposes ReCAST, a restoration-aware cascaded stage-wise training framework for classifying obfuscated Chinese SMS messages. It distills a large teacher model's de-obfuscation capability into a smaller deployable student by supervising obfuscated span detection, obfuscation type prediction, and text restoration, then uses the student for risk classification. On an internally constructed real-world Chinese SMS benchmark, ReCAST substantially outperforms directly trained baselines under obfuscation, targeting production latency and throughput constraints.

arXiv cs.CRSep 4, 08:36 UTC · 13d agoResearch

TLD Tracker: Exploring Newly Released Top

Unit 42 tracked 19 newly released top-level domains and found large-scale phishing, unwanted program distribution, and cybersquatting tied to TLD launch dates.

Researchers analyzed 19 new generic TLDs, including .zip, .bot, .ing, and .meme, released or approaching general availability over roughly 18 months. Data from passive DNS, registry zone files, newly registered domain feeds, and the Tranco top-1M list showed phishing campaigns, potentially unwanted program distribution, and domain squatting on these TLDs. Abuse correlated with each TLD's rollout phases, indicating attackers monitor general availability dates to register and weaponize domains. The IANA root database now lists over 1,000 generic TLDs.

Palo Alto Unit 42Aug 17, 12:47 UTC · Aug 17, 2026Research

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.

HuntressSep 10, 16:00 UTC · 6d agoResearch