OWASP LLM Top 10: Vaibhav Malik Voice Model Misuse


## Voice Model Misuse

**Author(s):Vaibhav Malik

### Description

As Large Language Models (LLMs), which are advanced AI models capable of processing and generating human-like language, become more sophisticated in generating realistic and expressive voices, there is a growing risk of these voice models being misused for malicious purposes. Voice model misuse refers to the unauthorized or unethical use of LLM-generated voices to deceive, manipulate, or harm individuals or organizations. This can lead to a range of negative consequences, including financial fraud, identity theft, reputational damage, and erosion of trust in AI-generated content.

LLMs can be trained on vast amounts of audio data, enabling them to generate voices that closely resemble authentic human voices, including those of celebrities or public figures. While this capability has many legitimate applications, such as virtual assistants or accessibility tools, it also opens up possibilities for misuse.

Malicious actors can exploit voice models to create convincing deepfakes, impersonating individuals to spread misinformation, commit fraud, or engage in social engineering attacks. For example, an attacker could use an LLM to generate a voice that mimics a company executive to authorize fraudulent financial transactions or manipulate stock prices. Other instances of voice model misuse include creating fake audio evidence for legal cases, generating misleading political campaign messages, or producing unauthorized voiceovers for commercial advertisements.

Voice model misuse can also have psychological and emotional impacts. Generating voices of deceased individuals or using voices without consent can cause distress to the individuals or their families. Impersonating trusted figures like politicians or journalists to spread fake news or propaganda can mislead the public and undermine trust in institutions.

Addressing voice model misuse requires a combination of technical safeguards, ethical guidelines, and legal frameworks. LLM developers and deployers have a responsibility to implement measures to prevent and detect misuse while also promoting transparency and accountability in the use of these powerful technologies.



### Common Examples of Risk

1. Deepfake scams: Attackers use LLM-generated voices to create convincing deepfakes of celebrities or public figures, using them to scam individuals or spread misinformation.
2. Voice phishing: Malicious actors generate voices that mimic trusted individuals, such as bank employees or government officials, to trick victims into revealing sensitive information or transferring funds.
3. Impersonation fraud: Criminals use LLM-generated voices to impersonate executives or employees, authorizing fraudulent transactions or manipulating business decisions.
4. Fake news and propaganda: Generated voices create convincing fake news videos or audio clips, spreading disinformation and manipulating public opinion.
5. Emotional manipulation: Voice models are used to generate the voices of deceased individuals or to create emotionally manipulative content without consent, causing psychological distress.

### Prevention and Mitigation Strategies

1. Consent and usage policies: Establish clear policies and guidelines for using voice models, requiring explicit consent from individuals whose voices are used in training data or generated content.
2. Watermarking and detection: Implement techniques to watermark generated voices and develop tools to detect and flag potentially fraudulent or manipulated voice content.
3. Authentication and verification: Use voice biometrics and other authentication methods to verify individuals' identities in voice-based interactions, reducing the risk of impersonation fraud.
4. Provenance tracking: Implement systems to track the origin and provenance of generated voice content. This will allow for easier identification of misuse and enable accountability.
5. Ethical guidelines and standards: Develop and adhere to ethical guidelines and industry standards for the responsible development and deployment of voice models, promoting transparency and mitigating potential harms.
6. Education and awareness: Educate the public about voice models' capabilities and limitations, helping individuals critically evaluate voice-based content and reduce susceptibility to manipulation.
7. Legal and regulatory frameworks: Collaborate with policymakers and legal experts to develop legal frameworks and regulations that address the misuse of voice models and provide clear consequences for malicious actions.
8. Monitoring and incident response: Implement monitoring systems to detect and respond to incidents of voice model misuse, enabling swift action to mitigate harm and hold perpetrators accountable.
9. Research and development: Invest in ongoing research to improve the robustness and security of voice models and develop techniques to detect and prevent misuse. This includes researching new methods for training voice models that are more resistant to manipulation, developing tools for detecting and flagging potential deepfakes, and exploring ways to make voice models more transparent and accountable.
Preventing voice model misuse is not a task that can be accomplished in isolation. It requires a collective effort. By fostering collaboration among LLM developers, researchers, policymakers, and industry stakeholders, we can share best practices, develop standards, and coordinate efforts to combat voice model misuse. Together, we can make a significant impact in ensuring the responsible use of these technologies.

### Example Attack Scenarios

Scenario #1: An attacker uses an LLM to generate a voice that closely mimics a celebrity, creating a deepfake video in which the celebrity appears to endorse a fraudulent investment scheme. The video is shared on social media, tricking individuals into falling for the scam.
Scenario #2: A cybercriminal generates a voice that impersonates a bank's customer service representative to call victims and manipulate them into revealing their account information or authorizing fraudulent transactions.
Scenario #3: A disgruntled employee uses an LLM to generate a voice that mimics the company's CEO, creating a fake audio recording in which the CEO appears to make controversial or damaging statements. The recording is leaked to the media, causing reputational harm to the company.
Scenario #4: A state-sponsored actor uses voice models to generate fake news videos featuring well-known journalists, spreading disinformation and propaganda to influence public opinion and undermine trust in the media.
Scenario #5: A malicious actor generates a voice that mimics a deceased individual, using it to create emotionally manipulative content and causing distress to the individual's family and loved ones.

### Reference Links


1. [OpenAI pulls its Scarlett Johansson-like voice for ChatGPT](https://www.theverge.com/2024/5/20/24160621/openai-chatgpt-gpt4