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Search: “identity services engine”

6 stories in the last 7d

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CategoryAllAdvisory 18Phishing & fraud 12Industry 11AI safety & security 11Exploit / PoC 11Threat actor 9Data breach 8Research 6AI tools & infra 4Tools 4Vulnerability 3Malware 2AI industry 2AI research 1
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Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection

Unit 42 clusters behavior of 40,000+ AWS identities from 125 cloud environments to map functional roles and enable lightweight SQL-based detection.

Palo Alto Unit 42 built an unsupervised behavioral clustering model using UMAP and HDBSCAN on AWS CloudTrail logs to map cloud identities to functional roles such as administrators, backup services, security tooling and DevOps. The study analyzed over 40,000 identities across 125 cloud environments over two months. The researchers show that heuristics extracted from the clustering map can be implemented in standard SQL, enabling role classification at scale without running a continuous ML pipeline. The methodology extends to audit logs from other cloud providers, SaaS and Kubernetes.

Palo Alto Unit 42 · 3d agoResearch

Getting a stranger’s phone kicked off the cellular network costs a few dollars

Researchers show attackers can remotely block strangers' phones and alarm gateways on US cellular networks by abusing lost/stolen IMEI reporting for $2.50-$4 per device.

Researchers from Michigan State University and three partner schools found six weaknesses in the lost/stolen device reporting ecosystem spanning devices, carrier systems, and cross-carrier block-list sharing. They demonstrated blocking unopened Samsung Galaxy Z Fold 7 phones and home alarm gateways on three major US carriers, with each block costing $2.50-$4 and taking roughly 20-80 seconds. The attacks exploit thin identity and ownership checks in prepaid accounts, IMEI leakage from vulnerable cellular chipsets used by two vendors with over 40% global market share, and pre-release IMEI databases purchasable for $600. Victims receive no notification, and restoring service requires proving device ownership to the carrier.

Help Net Security · 6d 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 Labs · 1d agoResearch in the wild1

Hackers Turn Windows Shadow Copies Into a Tool for Credential Theft and Ransomware

Attackers abuse Windows VSS shadow copies to delete recovery points before ransomware and to extract NTDS.dit/SAM credentials, demanding context-aware detection.

Threat actors are weaponizing the Volume Shadow Copy Service (VSS) for two purposes: destroying local recovery points via vssadmin.exe, wmic.exe, diskshadow.exe, wbadmin.exe, and bcdedit.exe before encryption, and accessing static copies of locked files such as NTDS.dit and SAM for credential theft. Ransomware families tied to shadow-copy removal include Akira, Black Basta, BlackCat, Conti, LockBit, Qilin, RansomHub, REvil, Ryuk, and WannaCry. Huntress's analysis of the Nightmare-Eclipse tool showed the BlueHammer technique chaining a Windows Defender timing issue with a VSS snapshot to dump the SAM database and decrypt NT hashes. Defenders should correlate VSS operations with parent process, hive access, and subsequent encryption activity, and maintain immutable off-host backups.

GBHackers · 2d agoResearch in the wild1

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

Linux Detection Engineering - Local Privilege Escalation

Elastic details a layered detection framework for Linux local privilege escalation, covering 2026's copy-on-write bug wave and LLM-assisted discovery.

Elastic Security Labs describes how most Linux local privilege escalations share a common host flow — an unprivileged process launched from a writable path becoming root — and proposes layered detections combining general outcome-based rules with per-technique rules in Elastic Defend and Auditd. It tracks 13 recent LPE disclosures, seven of which share a copy-on-write/zero-copy bug class, including Copy Fail, DirtyFrag, Fragnesia, DirtyDecrypt, DirtyClone, pedit COW, and RefluXFS. Qualys attributes RefluXFS to an LLM-assisted research effort with Anthropic using Claude Mythos Preview, and another bug is credited to an LLM-assisted workflow. Detection and endpoint rules are published in Elastic's detection-rules and protections-artifacts repositories.

Elastic Security Labs · 6d agoResearch1