OWASP LLM Top 10: AdsDawson ModelInversion

## Model Inversion

**Author(s):** [Ads - GangGreenTemperTatum](https://github.com/GangGreenTemperTatum)

### Description

Model Inversion attacks enable attackers to reconstruct sensitive training data by querying the Large Language Model (LLM) and analyzing its responses. By systematically probing the model, attackers can infer details about the data it was trained on, leading to significant privacy and security risks. This vulnerability is particularly concerning when models are trained on sensitive or proprietary data, as it can lead to the exposure of confidential information.

### Common Examples of Risk

- **Data Breaches**: Reconstruction of sensitive personal data, leading to privacy violations and potential legal repercussions.
- **Intellectual Property Theft**: Exposure of proprietary business information, compromising competitive advantage and business strategies.
- **Regulatory Violations**: Breaching data protection regulations by exposing sensitive data, leading to fines and legal action.
- **Reputation Damage**: Loss of trust from customers and stakeholders due to the exposure of confidential information.
- **Increased Attack Surface**: Providing attackers with insights into the training data, which could be leveraged for further attacks.

### Prevention and Mitigation Strategies

- **Differential Privacy**: Implement differential privacy techniques to ensure that individual data points cannot be inferred from the model’s outputs and apply differential privacy techniques to protect individual data points.
- **Access Controls**: Restrict access to the LLM’s querying capabilities, allowing only authorized users to interact with the model.
- **Query Rate Limiting**: Limit the rate and complexity of queries to reduce the risk of model inversion attacks.
- **Anomaly Detection**: Monitor for suspicious querying patterns that may indicate an attempted model inversion attack and take appropriate action.
- **Regular Audits**: Conduct regular security audits and testing to identify and mitigate potential vulnerabilities related to model inversion.

### Example Attack Scenarios

1. Personal Data Reconstruction: An attacker queries an LLM with various inputs designed to reveal personal information about individuals used in the training data. Over time, the attacker can piece together enough information to reconstruct sensitive personal data, such as names, addresses, or medical records.
2. Proprietary Information Exposure: A competitor queries an LLM trained on proprietary business data with the aim of uncovering trade secrets or business strategies. By analyzing the responses, the competitor can infer valuable information that compromises the business's competitive advantage.
3. Identifying Training Data Sources: An attacker systematically queries the LLM to identify specific datasets or sources used during training. This can lead to the exposure of proprietary data sources or breach data licensing agreements.

### References

- [Model Inversion Attacks That Exploit Confidence Information and Basic Countermeasures](https://arxiv.org/abs/1506.05108)
- [Deep Learning with Differential Privacy](https://arxiv.org/abs/1607.00133)
- [Membership Inference Attacks Against Machine Learning Models](https://arxiv.org/abs/1610.05820)
- [CWE-200: Exposure of Sensitive Information to an Unauthorized Actor](https://cwe.mitre.org/data/definitions/200.html)
- [CWE-203: Information Exposure Through Discrepancy](https://cwe.mitre.org/data/definitions/203.html)