Search: “Applied Systems Engineering”

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

CategoryAll Vulnerability 657 Ransomware 266 Malware 238 Exploit / PoC 236 Policy & legal 146 Industry 129 Data breach 121 Phishing & fraud 111 Threat actor 109 AI safety & security 59 Research 51 AI tools & infra 42 Advisory 23 AI industry 21

SeverityAll critical 1074 high 404 medium 374 low 232 info 163

Hunting Vulnerabilities Using Frontier Models

Okta used frontier AI models GPT-5.5 Cyber and Mythos via OpenAI and Anthropic programs to scan millions of code lines for vulnerabilities.

Okta describes using frontier AI models, including GPT-5.5 Cyber Preview (TAC) and Mythos Preview, through OpenAI's Daybreak Cyber Partner Program and Anthropic's Project Glasswing to hunt vulnerabilities across its product codebase. The team built a custom Python orchestrator with strong isolation, vendor-agnostic model support, and four distinct scanning pipelines executed as isolated Codex or Claude Code sessions with progressive context loading to reduce context bloat. Human experts and AI agents worked both autonomously and in paired hunts, and Okta reports the best results when humans and agents taught each other.

Okta SecuritySep 8, 00:00 UTC · 9d agoResearch

Introducing Unit 42’s Attribution Framework

Unit 42 releases its Attribution Framework, a systematic method using Diamond Model and Admiralty scores to attribute activity clusters to named threat actors.

Palo Alto Networks' Unit 42 introduced a structured framework for threat actor attribution built on the Diamond Model of Intrusion Analysis and Admiralty reliability/credibility scoring. The framework tracks activity at three levels: activity clusters (named CL-STA, CL-CRI, CL-UNK, or CL-MIX), temporary threat groups, and named threat actors using the constellation naming schema. Analysts score evidence across TTPs, tooling, malware code, OPSEC, infrastructure, timelines, and victimology to decide when to merge or elevate clusters, avoiding premature group naming.

Palo Alto Unit 42Aug 17, 10:52 UTC · Aug 17, 2026Research

Securing the unpatchable in an age of AI-driven vulnerabilities

Cisco Talos argues AI-driven vulnerability discovery leaves unpatchable OT systems exposed, recommending virtual patching via NGFW/IPS and micro-segmentation.

AI-assisted code analysis is uncovering vulnerabilities faster than organizations can patch, leaving certified or end-of-life OT systems with unmitigated known flaws. Talos recommends virtual patching with next-generation firewalls and IPS, micro-segmentation using VLANs and ACLs, and building visibility-based inventories of legacy systems. The article cites WannaCry's impact on the NHS and 2023 exploitation of end-of-life software in government systems, and warns that air gaps and data diodes are routinely circumvented by operational shortcuts.

Cisco TalosSep 16, 10:00 UTC · 18h agoResearch

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

Harnessing LLMs for Automating BOLA Detection

Unit 42's BOLABuster methodology uses LLMs to automate detection of broken object-level authorization vulnerabilities, uncovering flaws in Grafana, Harbor, and Easy!Appointments.

Palo Alto Unit 42 details BOLABuster, a methodology combining large language models with heuristics to automate detection of broken object-level authorization (BOLA) flaws, which traditional fuzzing and static analysis struggle to find. The approach uses LLM reasoning to understand application logic, map endpoint dependency relationships, and generate and interpret test cases. It found CVE-2024-1313 in Grafana, CVE-2024-22278 in Harbor, and 15 CVEs in Easy!Appointments. The team is continuing to hunt for BOLAs in open-source and internal projects.

Palo Alto Unit 42Aug 17, 12:34 UTC · Aug 17, 2026Research CVE-2024-1313 CVE-2024-22278 CVE-2023-3285+14 CVEs

Your threat feed is someone else's database: What ingesting malware intel at scale takes

GitHub's Dependabot lead shares five production lessons for ingesting community malware intelligence feeds across eight package ecosystems at scale.

GitHub's Dependabot team monitors over 30 million repositories and extended malicious-package advisories from npm to eight package ecosystems by ingesting OpenSSF's malicious-packages intelligence. The team catalogued roughly 18 new malicious npm packages per day in the year ending May 2026. The write-up argues that provenance with batch reverts, fingerprinting to catch echo-chamber duplicates, and heavyweight normalization are the make-or-break engineering for feed ingestion. It also recommends automated publishing with import caps and anomaly flagging, and quarantining malformed records rather than silently repairing them.

Help Net SecuritySep 3, 00:00 UTC · 14d agoResearch1

AD Rights Management Service (Part 1): Architecture, Deprecation, and Reconnaissance

Huntress maps AD RMS architecture and recon paths, setting up an offline key-extraction attack against its unrotatable 255-year root key.

Huntress's multi-part research examines Active Directory Rights Management Services, Microsoft's enterprise DRM role that still ships in Windows Server 2025 despite migration guidance favoring Azure Information Protection. Part 1 covers the trust model and how an ordinary domain account can locate RMS clusters and read rights-policy templates. Part 2 will detail four independent paths for extracting the Server Licensor Certificate (SLC) private key via service-group membership and the configuration database, enabling fully offline decryption of protected documents. The SLC certificate is valid from 2002 to 2258 and has no key-rotation mechanism, so a stolen key decrypts protected content indefinitely.

HuntressSep 8, 04:00 UTC · 9d agoResearch1

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

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

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 42Sep 14, 10:00 UTC · 2d agoResearch

Why Johnny Can't Encrypt: A Usability Evaluation of PGP 5.0 (1999)

Seminal 1999 USENIX study finds most novice users cannot correctly sign and encrypt email with PGP 5.0 in 90 minutes.

Whitten and Tygar's USENIX Security Symposium paper evaluates whether cryptography novices can use PGP 5.0 effectively, using cognitive walkthrough analysis and a laboratory user test. The majority of test participants failed to successfully sign and encrypt a message within 90 minutes, despite PGP 5.0 having a well-regarded graphical interface. The authors argue that security requires usability standards beyond those of general consumer software and propose domain-specific UI design principles for security. The paper is a foundational reference in usable security research.

Lobsters · securitySep 9, 06:47 UTC · 7d agoResearch

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 agoResearch1