Search: “sensitive data exposure”

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

CategoryAll Vulnerability 535 Data breach 454 Ransomware 336 Policy & legal 229 Malware 148 Exploit / PoC 132 Industry 102 Phishing & fraud 84 Threat actor 78 AI safety & security 58 Research 23 AI tools & infra 22 Advisory 15 Tools 11

SeverityAll critical 1125 high 542 medium 294 low 191 info 104

Getting ahead of ‘harvest-now-decrypt-later’: Post-quantum cryptography planning

Opinion piece urges organizations to begin post-quantum cryptography migration now, citing harvest-now-decrypt-later risk and NIST deadlines.

CSO Online outlines why harvest-now-decrypt-later makes long-lived sensitive data a current risk even before quantum computers exist. It cites NIST IR 8547 timelines deprecating RSA-2048 and ECC P-256 by 2030 and removing them by 2035, finalized FIPS standards ML-KEM, ML-DSA, and SLH-DSA, upcoming FN-DSA (FIPS 206), NSA requirements for national security systems from 2027, and UK NCSC phased guidance through 2035. The author recommends cryptographic discovery, crypto-agility, and prioritizing long-confidentiality data and TLS endpoints.

CSO OnlineSep 10, 09:00 UTC · 6d agoResearch

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

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 NewsSep 12, 10:24 UTC · 4d agoResearch2

Your Cloud Security Checklist Doesn't Work the Way You Think It Does

Intruder's 2026 Cloud Security Index found misconfiguration risk profiles differ sharply across AWS, Azure, and Google Cloud across 3,000 organizations.

Intruder analyzed misconfiguration data from 3,000 organizations across AWS, Azure, and Google Cloud for its 2026 Cloud Security Index. Weak IAM controls and missing logging affected 80-98% of accounts regardless of provider, while exposed services ranged from 76% on AWS to just 8% on Google Cloud. Top issues included S3 buckets without HTTPS enforcement (87% of AWS accounts), Entra ID users without MFA (55% of Azure accounts), and missing OS Login MFA (77% of Google Cloud accounts). Weak IAM prevalence rose with organization size, from 87% at SMEs to 98% at large enterprises, and midmarket organizations took the longest to remediate at 35 days on average.

The Hacker NewsSep 7, 11:45 UTC · 9d agoResearch

Mapping out your unknown: A threat hunter’s guide to GitHub

Datadog Security Labs publishes a threat-hunting guide with audit-log queries to detect GitHub token theft, device code phishing, and source code exfiltration.

Datadog's threat-hunting guide covers GitHub audit log queries for detecting compromised accounts, stolen personal access tokens, and malicious OAuth app authorizations. Attackers typically obtain credentials through phishing, credential stuffing, leaked secrets, or device code phishing, then map private repositories, exfiltrate source code, and pivot into connected cloud and CI/CD environments. The guide maps detections to MITRE techniques like T1078 and T1528 and documents GitHub logging quirks affecting attribution, token metadata, and visibility fields.

Datadog Security LabsSep 16, 00:00 UTC · 1d agoResearch in the wild1