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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.
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BigBear Microsoft 365 phishing service bypassed MFA at 258 organizations
CloudSEK found the BigBear 2.0 phishing-as-a-service platform bypassed MFA at 258 organizations and captured over 5,000 Microsoft 365 credentials.
CloudSEK researchers gained administrator access to the BigBear 2.0 phishing-as-a-service control panel and found it had exfiltrated 5,137 credential records, including 474 completed MFA-bypassed authentications, 1,032 plaintext passwords, and 4,148 session cookies across 3,331 victim IPs in 40+ countries. The Evilginx2-based AitM proxy intercepts credentials and authenticated session cookies, and custom JavaScript interferes with FIDO2/WebAuthn to force weaker authentication. The panel is leased to at least five affiliate operators via Telegram exfiltration bots, and geo-matched residential proxies cover 69 countries to evade detection.
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