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

CategoryAll Vulnerability 300 Malware 269 Exploit / PoC 175 Ransomware 169 Data breach 109 Policy & legal 104 Industry 99 Threat actor 99 Phishing & fraud 60 AI tools & infra 28 Research 25 AI safety & security 21 AI industry 14 Tools 14

SeverityAll critical 617 high 343 medium 281 low 174 info 97

The modern attack chain: Rethinking Google Workspace security in the age of AI

Analysis of Vercel and Composio breaches shows stolen OAuth tokens, not phishing email, now open Google Workspace attacks — a path authorized AI agents follow.

The author analyzes the Vercel and Composio breaches as the same OAuth-first attack chain run twice: a stolen OAuth token, obtained via a compromised supplier, becomes the entry point rather than email. These tokens survive password resets, are hard to observe, and let attackers read Gmail and Drive data, take over accounts, and pivot laterally using stored credentials and password-reset magic links. The piece warns that authorized AI agents with overbroad OAuth grants can unintentionally traverse the same path — accessing inboxes, reading sensitive content, and exfiltrating data downstream — without any malicious actor or compromised credential.

Help Net SecuritySep 16, 05:00 UTC · 1d agoResearch in the wild

Beneath the Surface: Detecting and Blocking Hidden Malicious Traffic Distribution Systems

Unit 42 built an ML-based detector for malicious traffic distribution systems, finding malicious TDS chains average longer redirections and more URLs than legitimate ones.

Traffic distribution systems redirect victims through chains of intermediate domains to hide final destinations, serving phishing, malvertising, and online gambling operations. Unit 42's topological analysis of redirection graphs found malicious TDS traffic uses longer chains (about 25% exceed four hops vs 10% benign), more URLs (median 126 vs 80), and fewer isolated subgraphs with higher connectivity. These features power an ML detector integrated into Advanced DNS Security and Advanced URL Filtering to identify and block malicious TDS infrastructure in customer traffic.

Palo Alto Unit 42Aug 17, 12:46 UTC · Aug 17, 2026Research

Attack Paths Into VMs in the Cloud

Unit 42 maps attack paths into AWS, Azure, and GCP VMs through intended features like startup scripts and SSH key pushes.

Palo Alto Unit 42 reviewed attack vectors against virtual machine services on AWS, Azure, and GCP, finding that 11% of internet-exposed cloud hosts carry Critical or High severity vulnerabilities. The attack paths rely on legitimate features such as EC2 User Data, VM custom data, EC2 Instance Connect, SSM Run Command, and serial consoles rather than vulnerabilities, and exploiting them requires attackers to first obtain control plane permissions. A compromised VM exposes not only its data but the workload identity and cloud permissions assigned to it, making identity compromise potentially more damaging than data theft. The firm places mitigation responsibility on cloud users and administrators.

Palo Alto Unit 42Aug 17, 12:36 UTC · Aug 17, 2026Research1

The AI Malware Maturity Gap

Recorded Future introduces AIM3, a five-level maturity model for AI malware, showing current attacker AI use is mostly AI-assisted rather than autonomous.

Recorded Future proposes AIM3, a five-level model defining AI malware from LLM-translated to LLM-embedded, spanning experimentation to fully autonomous agentic campaigns. Public examples remain early-stage: PROMPTFLUX uses Google Gemini to rewrite its VBScript dropper (Level 1), while Lamehug/PROMPTSTEAL, attributed to APT28, invokes the HuggingFace API to generate reconnaissance commands (Level 3). The authors argue most current AI malware augments existing tradecraft rather than enabling one-click autonomous attacks.

Recorded FutureAug 26, 00:00 UTC · 22d agoResearch

Enterprise Defenses Recovered at the Edge and Collapsed Inside

Picus Labs' Blue Report 2026 finds perimeter prevention at 69% but post-compromise prevention just 37%, with reconnaissance blocked only 10% of the time.

Picus Labs' Blue Report 2026, based on 434,000+ simulated attacks across client production environments in H1 2026, found perimeter prevention effectiveness rose from 62% to 69% while the Post-Compromise Prevention Rate was only 37%. Quiet techniques fared worst: reconnaissance was blocked 10% of the time, registry-based credential access less than 1%, and the alert score stayed at 14% despite logging at a four-year high of 58%. IOC-based prevention fell to 50% from 71% in 2024, and Mimikatz's LSASS path was blocked about 94% while alternative credential-read paths went nearly undetected.

The Hacker NewsAug 12, 11:41 UTC · Aug 12, 2026Research