Voice deepfakes are no longer a futuristic curiosity. Over the last five years criminals and trolls have moved from experimental clips to highly targeted vishing (voice-phishing) campaigns that use cloned voices to defraud organisations, influence voters, or coerce staff into taking action. As defenders, we must both understand the offensive toolset used to create these attacks and build realistic, ethical simulations to harden people and processes. This article surveys the common tools used to generate voice deepfakes, shows real-world case studies, and gives pragmatic guidance for safe, high-value simulation programs.

Why simulate deepfake-voice attacks?

Deepfake vishing combines social-engineering craft with synthetic audio to exploit two core human weaknesses: trust in a familiar voice, and urgency created by social proof (a senior asking directly). Simulating these attacks in a controlled environment lets you:

• Test verification processes (call-backs, out-of-band confirmation).

• Measure human reaction under pressure and design targeted training.

• Validate telephony and email security controls (caller ID, line recording analysis).

• Exercise incident response: detection, escalation, legal/comms workflows.

Given the rapid evolution of audio AI, simulations must use the same class of tools adversaries use — or higher fidelity — to be realistic.

The offensive toolkit — what attackers (and simulation teams) commonly use

Below are categories of widely used tools and specific examples you'll encounter when building realistic simulations.

1. Consumer-to-prosumer cloning platforms (high fidelity, easy to use)

• ElevenLabs, Descript (Overdub) and Resemble AI — these provide fast voice cloning from short audio samples and straightforward UIs or APIs for programmatic generation. They are commonly cited in analyses of misuse and real-world incidents.

2. Studio-grade services (emotion, prosody control)

• Respeecher, Resemble, Resemble’s enterprise offerings — targeted at media and entertainment, these services focus on emotional transfer and large-scale voice rendering, making them attractive to attackers who want nuance.

3. Open research models and academic tech

• VALL-E (Microsoft) and other neural speech synthesis models give researchers (and skilled adversaries) the ability to craft realistic prosody and style with smaller datasets. These are generally more technical to operate but can produce very convincing output.

4. Lightweight and free tools / hobbyist sites

• Sites such as FakeYou, Voicemod, and other text-to-speech playgrounds let low-skill actors create impersonations quickly. Lower fidelity but often “good enough” for scams. (Commonly misused in voter-influence and harassment campaigns.)

5. Hybrid workflows and editing tools

• Attackers often combine cloning with DAW (digital audio workstation) edits, background noise layering, and compression to mimic phone-line artifacts and evade casual detection. Tools like Descript are useful for editing and polishing.

Real-world examples (what happened and what we learned)

CEO impersonation that cost six figures (2019)
A widely reported incident saw a UK energy firm transfer roughly $243,000 after an attacker used an AI-generated voice mimicking the CEO’s tone and phrasing to instruct a subordinate to make an urgent transfer. This case is often cited as the first high-profile demonstration of voice-cloning fraud and highlights the power of context and authority over technical scepticism.

Political robocalls and platform misuse (2024)
In the run-up to the New Hampshire primary, automated calls using a synthetic voice purportedly of President Biden were traced by researchers to tools available in the open market. The incident prompted law-enforcement and platform action and highlighted how accessible these tools are to large-scale misinformation campaigns.

Targeting large enterprises (2024 WPP incident)
Attackers combined a cloned voice with social platforms and edited video to create credible interactions that targeted senior corporate teams; large agencies reported attempted fraud and disruption. This shows attackers will integrate multiple modalities — voice, video, and fabricated messaging — making simulations that only test single channels less effective.

What these cases teach us: attackers exploit real organisational processes — urgent payments, approval chains, and trust relationships. High-fidelity voice alone isn’t always necessary; it amplifies social engineering already in play.

Building ethical, realistic deepfake vishing simulations

1. Threat modeling first
Map who could be impersonated (C-suite, finance, HR) and what controls exist. Prioritise scenarios with the greatest impact (wire transfers, payroll changes). Use red-team insights and historical incidents to shape scenarios.

2. Use the right tool for realism
Match attacker sophistication: for low-sophistication phishing, hobbyist TTS is fine; for executive impersonation, use a high-fidelity cloning platform (test accounts with vendors or synthetic voices you own). Keep legal/ethical approvals and consents in place — never clone a real employee’s voice without documented permission. (Policy and consent are non-negotiable.)

3. Add contextual realism
Combine voice with caller ID spoofing, timely social media posts, or a preceding spear-phish email. Simulations that isolate the voice but ignore the surrounding context under-test the organisation.

4. Measurement and telemetry
Instrument every simulation: call recordings, decision timestamps, verification steps taken, and follow-up surveys. Quantify not just clicks or transfers, but decision quality: did the target use the prescribed out-of-band check? How long before escalation?

5. Detection and detection-driven exercises
Include automated detection testing: pass synthetic audio through your telephony and forensics pipelines to validate deepfake detection thresholds and false positives. Several vendors (including some TTS providers) offer detection services you can integrate into CI/CD for security monitoring.

6. Remediation and learning
After each exercise run targeted training and process fixes: enforce mandatory callback policies for transfers, implement positive authentication phrases, require multi-person approval, and harden vendor/payment workflows.

Defensive controls you should prioritise

• Out-of-band confirmation (video call or callback to a pre-registered number) for high-risk requests.

• Multi-party approvals and “two eyes” for financial actions.

• Call recording + forensic analysis — keep artifacts for post-incident triage.

• Telemetry and anomaly detection — flag calls that deviate from normal patterns (timing, phrasing).

• Vendor-level mitigations — require suppliers to adopt least-privilege and time-bound payment instructions.

• Employee training that emphasises verification rituals over voice recognition alone.

Legal, ethical, and vendor considerations

• Only use voice cloning in simulations with documented approvals and clear boundaries. Clone synthetic test voices or consented voices. Unauthorised cloning exposes you and your organisation to legal and reputational risk.

• If you engage vendor platforms for simulation, review their policies on misuse, logging, and detection support. Many mainstream vendors now offer enterprise safety features and misuse reporting.

Closing: simulated realism, responsibly delivered

Deepfake voice tech will continue to improve. For defenders, the core work is unchanged: anticipate how attackers will combine new capabilities with classic social engineering, then design simulations and controls that break the attack chain at procedural and technical points. Run high-fidelity exercises, but do so with clear ethics, consent, and measurement. That’s how you turn scary demos into actionable resilience.

If you’d like, ClearPhish can help design a tailored deepfake-vishing tabletop and red-team exercise that uses realistic toolchains while ensuring legal and privacy guardrails — and produce a focused remediation playbook for finance and executive teams.