Search: “mapping”

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

CategoryAll Exploit / PoC 177 Vulnerability 90 Ransomware 58 Malware 55 Threat actor 42 Data breach 23 Phishing & fraud 16 Advisory 8 Policy & legal 7 AI safety & security 6 Research 5

SeverityAll critical 343 high 107 medium 31 low 2 info 4

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

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

What Zero-Day Response Should Be in the Post-Mythos Era

Picus Security outlines a zero-day response playbook where defenders simulate exploit technique chains before public PoCs exist.

The article uses PaperCut NG/MF's August incident — exploitation in the wild before any patch, with the first emergency fix bypassed the same day and a third landing September 1 — as the template for AI-accelerated vulnerability response. It walks through a hypothetical CVE-2026-1001 (explicitly made up) to argue defenders should map CVEs to ATT&CK technique chains and simulate them against NGFW, WAF, EDR, endpoint hardening, and SIEM controls within minutes of disclosure. It notes disclosure-to-exploitation time has fallen from 21.5 days to hours.

BleepingComputerSep 15, 13:45 UTC · 1d agoResearch in the wildCVE-2026-1001

AI AppSec tools agree on just 5% of security findings

Contrast Security report finds 42 confirmed exploit attempts per application monthly and three AI scanners agreeing on only 5% of findings.

Contrast Security's AppSec Overflow 2026 report, drawing on telemetry from hundreds of thousands of production applications and APIs, found adversaries touch the average application every four minutes with 42 confirmed viable exploit attempts per application monthly, led by untrusted deserialization, path traversal, and method tampering. Legacy flaws Log4Shell and Spring4Shell remain widespread, mean time to exploit fell from over two years in 2018 to under three weeks for most 2025 exploited vulnerabilities, and average critical fix time is 92 days. Three AI scanners set on the same codebase agreed on only 5% of findings, and triaging a 2-million-line codebase scan cost roughly $128,000 versus $315 in API charges. Among exploited CVEs in the dataset, 82% of KEV-listed entries carried EPSS scores of 90% or higher, while CVE-2006-1547 and CVE-2023-38180 were confirmed exploited despite EPSS scores under 25%.

Help Net SecurityAug 31, 00:00 UTC · 17d agoResearch in the wildCVE-2006-1547 CVE-2023-381801

Automatically Detecting DNS Hijacking in Passive DNS

Unit 42's machine learning pipeline detected 6,729 DNS hijacking events between March and September 2024, hitting political parties, ISPs, and universities.

Unit 42 processes roughly 167 million new DNS records daily and applies a machine learning model using 74 features over 169 TB of passive DNS and geolocation data to flag hijacked domains. From March to September 2024 the pipeline screened over 29 billion records and classified 6,729 as DNS hijacking, averaging 38 detections per day; a new model detects hijacks in customer traffic within about 10 minutes. Notable cases include a Hungarian political party's hijacked domain, defacement of a large utility company and ISP, and university and research center domains repurposed for illicit gambling. DNS hijacking typically relies on stolen registrar or DNS provider credentials or cache poisoning, enabling MitM attacks, phishing, drive-by downloads, and scams.

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