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Breacher.ai — A consented, generated adversarial voice-call corpus. Every campaign produces paired synthetic-and-real telephone audio: a cloned voice with signed likeness authorisation holds a live two-way conversation with an employee, answers questions, and independently handles inbound callbacks, running through to the help-desk password-reset or finance-approval step. Each session therefore yields full-call audio with speaker turns, the pretext and script used, the verification challenge issued, which specific control step was skipped or satisfied, the final process outcome, and — critica…
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A consented, generated adversarial voice-call corpus. Every campaign produces paired synthetic-and-real telephone audio: a cloned voice with signed likeness authorisation holds a live two-way conversation with an employee, answers questions, and independently handles inbound callbacks, running through to the help-desk password-reset or finance-approval step. Each session therefore yields full-call audio with speaker turns, the pretext and script used, the verification challenge issued, which specific control step was skipped or satisfied, the final process outcome, and — critically for deepfake detection — ground-truth labels on exactly which audio segments are synthetic. Published evidence of what the corpus shows: 63% of targets could not distinguish synthetic voice or video from genuine during the call, and 78% of assessed organisations were rated highly vulnerable when measured as process failure rather than individual error
From 3 yearsCoverage Information TechnologyAsset class Equities
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Breacher.ai — A consented, generated adversarial voice-call corpus. Every campaign produces paired synthetic-and-real telephone audio: a cloned voice with signed likeness authorisation holds a live two-way conversation with an employee, answers questions, and independently handles inbound callbacks, running through to the help-desk password-reset or finance-approval step. Each session therefore yields full-call audio with speaker turns, the pretext and script used, the verification challenge issued, which specific control step was skipped or satisfied, the final process outcome, and — critica…
Breacher.ai offers (Alternative, Sentiment) — Session-scale rather than mass-scale but extremely rich per record: at 150 concurrent sessions per window, multiple concurrent campaign windows per engagement, and multi-engagement history, estimate tens of thousands of consented adversarial call sessions accumulated, each carrying full two-party audio plus step-level outcome labels. At even 3 minutes per call that is tens of thousands of hours of labelled conversation-grade synthetic-and-genuine telephony audio. The vendor also states a proprietary 'Social Engineering Risk Index', implying a cross-organisation outcome panel already exists — request further research for the true session count and retained-audio hours.
Training and benchmarking audio deepfake and synthetic-voice detectors on consented, honestly-labelled data rather than scraped public figures; telephony fraud detection and real-time call-screening models; voice-print liveness and anti-impersonation systems; building and evaluating verification-procedure automation for help desks and finance approval; social-engineering risk scoring; and red-team benchmark suites for voice AI safety
The data is with 3 years of history.
Coverage spans US; Application Software; alternative, sentiment; equities.
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