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DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllMalware 100Vulnerability 66Ransomware 56Policy & legal 52Data breach 49Industry 48Phishing & fraud 41Threat actor 39Exploit / PoC 29AI tools & infra 8AI industry 5Research 5Advisory 4AI research 4
SeverityAllcritical 169high 149medium 91low 82info 19

Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

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

Creepy crawlies

git.kernel.org spends more CPU rendering commit pages for abusive AI-era scrapers than on all legitimate access including git clones.

Konstantin Ryabitsev reports that abusive crawler 'background radiation' at git.kernel.org consumes more CPU cycles rendering commit HTML than all legitimate access, with 14 cores across five geo-distributed nodes dedicated to scraper traffic. Simon Willison highlights the implications for crawlable web services like Datasette.

Simon Willison · 9d agoAI industry1