Deepfake Penetration Testing
Your fraud controls were built for a threat model that no longer holds.
A technical assessment of identity verification and fraud detection systems. We generate current-generation synthetic faces, documents, video, and voiceprints, then submit them through your real controls: liveness detection, document verification, face matching, video KYC, and voice authentication. You get per-control acceptance rates, not an opinion. Fully managed and external, nothing installed.
Built for banks, payments, and digital identity providers.
Why banks are the most exposed
Remote identity verification scaled faster than the controls behind it matured.
The threat model is out of date
Liveness detection, document checks, and voice authentication were specified when synthetic media was expensive, slow, and visibly imperfect. It is now cheap, real-time, and interactive. The control did not change. The attack did.
Verification moved to remote channels
Remote account opening, video KYC, and call-centre voice authentication put identity decisions on a camera and a microphone. Both are exactly the surfaces synthetic media is best at defeating.
Vendor benchmarks are not your results
Detection vendors benchmark against public datasets that current generation tooling has moved past. A published accuracy figure is not a measurement of your stack against media made this month.
What we submit, and what it proves
Six control surfaces. Run the full set for a complete picture, or scope down to the layer you are least sure about.
We submit current-generation synthetic faces against your liveness check: passive, active, and challenge-response. The question is not whether the system rejects a printed photo. It is whether it rejects a real-time rendered face that blinks, turns, and follows prompts.
Synthetic identity documents paired with a matching synthetic face, submitted through your document capture and comparison pipeline. Tests whether document authenticity checks and biometric face-match hold when both artifacts are generated together and made consistent.
A live conversational avatar submitted into your remote onboarding or video KYC session. It responds to prompts, turns on request, and holds a conversation with your agent or automated flow, which is exactly the condition these controls were never designed for.
Cloned voiceprints submitted against your voice authentication and call-centre verification. Clone time under five minutes from publicly available audio. Tests both automated voice biometrics and the step-up path behind them.
Synthetic media delivered through a virtual camera or injected stream rather than presented to a physical sensor. Injection bypasses presentation-attack detection entirely, and most verification stacks have no control at this layer.
Your existing deepfake detection vendor, tested against media generated with current tooling rather than the public datasets it was benchmarked on. Establishes real-world detection rate and where it degrades.
How the assessment runs
The technical variant of OSES™, our orchestrated simulation framework. Where an OSES™ engagement targets process and decision-making, this one targets the verification stack directly. Same discipline on scoping and evidence, different surface.
Every engagement runs under signed authorization against your own systems, with agreed environments, submission volumes, named approvers, and documented abort conditions. Client names never appear in our public material. For engagements targeting process and decision-making instead of systems, see deepfake phishing simulation.
Regulated identity, at volume
Organizations making identity decisions remotely, at scale, where a false accept is a loss event and a regulatory one.
What makes this different
Current-generation media
Artifacts are generated with tooling available right now, not archived research datasets. If your detection was benchmarked in 2024, this is the first honest measurement you will get.
Injection as well as presentation
Most assessments stop at presenting media to a camera. We also test stream injection, which is where the majority of verification stacks have no control at all.
Numbers, not narrative
Per-control acceptance rates and the confidence thresholds at which behaviour changes. Evidence a fraud team can act on and a regulator will accept.
Real threats, real testing, real findings
A cloned voiceprint submitted against verbal verification controls, and what it revealed about the step-up path behind them.
Read the case study Agentic AIAutonomous agents driving synthetic media generation and submission end to end, with no human operator in the loop.
Read the case studyCommon questions
What is deepfake penetration testing?
An authorized technical assessment that submits AI-generated synthetic media through an organization's real identity verification and fraud detection systems to establish whether those controls accept it. It is a systems test, not an awareness exercise. The output is per-control pass and fail data with threshold recommendations.
Why are banks especially exposed?
Because banking moved identity verification to remote channels faster than the controls matured. Remote account opening, video KYC, and voice authentication in the call centre were all designed against a threat model that assumed synthetic media was expensive and imperfect. It is now neither, and 42 percent of financial sector fraud attempts are already AI-driven.
Does this test our staff?
No. This assessment targets systems: liveness detection, document verification, face matching, voice biometrics, video KYC, and the detection tooling behind them. If you want the human and process layer tested instead, that is a separate engagement.
What is stream injection and why does it matter?
Presentation-attack detection assumes media is presented to a physical camera. Injection delivers synthetic video directly into the capture pipeline through a virtual camera or a compromised SDK, bypassing that assumption entirely. Most verification stacks have strong presentation controls and no injection controls, which makes it the highest-yield vector we test.
Is this authorized, and how is scope controlled?
Every engagement runs under signed authorization against your own systems, within an agreed scope, environment, and window, with named approvers and documented abort conditions. Testing is typically run against staging or a controlled production segment, and submission volumes are agreed in advance.
Will this disrupt operations?
No. The assessment runs externally and fully managed, with no software installation and no integration into your stack. Volumes and windows are set so that queue times and review workload stay within normal range.
What do we receive at the end?
A Deepfake Vulnerability Report covering per-control acceptance rates, the confidence thresholds at which behaviour changes, which vectors your stack has no coverage for, prioritized remediation, and evidence written for auditors and regulators without a rewrite.
How long does an engagement take?
Two to three weeks from scoping call to final report, with the submission window itself usually a few days inside that.
Find out what your controls actually accept
Thirty minutes. We will walk through your verification stack and identify which layers are worth submitting against first.
Or read the full assessment methodology.
support@breacher.ai
