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LLM07:2025 System Prompt Leakage

Description​

System Prompt Leakage Risk

System Prompt Leakage is a critical vulnerability where an LLM's internal workings or training data are inadvertently exposed through its outputs or interactions. This exposure can encompass sensitive information disclosure, training data exposure, model architecture leaks, internal state revelations, and data poisoning vulnerabilities.

The risk carries particular significance due to several inherent characteristics of LLMs. These systems can memorize and inadvertently regurgitate sensitive data, while their training data may contain confidential information that shouldn't be exposed. Furthermore, model architecture leaks can compromise valuable intellectual property, and exposed internal states can be exploited for various types of attacks. Information leakage can occur through multiple channels, some of which may not be immediately apparent.

Effective mitigation of system prompt leakage requires a comprehensive approach. This includes implementing robust data anonymization techniques, protecting model architecture through encryption and access controls, securing internal state information, implementing sophisticated output filtering mechanisms, and conducting regular security audits to identify potential vulnerabilities.

It's crucial to note that even well-protected models can leak information through subtle channels that may not be immediately obvious. This necessitates ongoing vigilance and regular assessment of potential information exposure pathways.

Common Examples of Vulnerability​

1. Sensitive Information Disclosure​

  • Personal data exposure
  • Confidential information leaks
  • Intellectual property theft
  • Trade secret exposure

2. Training Data Exposure​

  • Data poisoning attacks
  • Model inversion attacks
  • Training data memorization
  • Data leakage through outputs

3. Model Architecture Leaks​

  • Intellectual property theft
  • Model architecture reverse-engineering
  • Internal state exploitation
  • Model stealing

4. Internal State Revelations​

  • Internal state exploitation
  • Model inversion attacks
  • Side-channel attacks
  • Data leakage through internal state

5. Data Poisoning Vulnerabilities​

  • Data poisoning attacks
  • Model corruption
  • Backdoor attacks
  • Trojan attacks

Prevention and Mitigation Strategies​

1. Data Anonymization​

  • Data masking
  • Data encryption
  • Data anonymization techniques
  • Data access controls

2. Model Architecture Protection​

  • Model architecture encryption
  • Model architecture obfuscation
  • Access controls
  • Secure model deployment

3. Internal State Encryption​

  • Internal state encryption
  • Secure internal state storage
  • Access controls
  • Regular security audits

4. Output Filtering​

  • Output filtering techniques
  • Data leakage detection
  • Anomaly detection
  • Regular security audits

5. Regular Security Audits​

  • Regular security audits
  • Penetration testing
  • Vulnerability assessments
  • Compliance audits

Example Attack Scenarios​

Scenario #1: Data Leakage​

An attacker exploits a vulnerability in the model's output filtering mechanism to extract sensitive information.

Scenario #2: Model Inversion Attack​

A malicious actor uses a model inversion attack to reconstruct the model's training data, including sensitive information.

Scenario #3: Internal State Exploitation​

The model's internal state is exploited to launch a side-channel attack, revealing confidential information.

Scenario #4: Data Poisoning Attack​

An attacker poisons the model's training data, causing it to produce biased or incorrect outputs.

Scenario #5: Model Stealing​

The model's architecture is stolen and used to create a competing model, violating intellectual property rights.

  1. Data Leakage Prevention
  2. Model Architecture Protection
  3. Internal State Encryption
  4. Output Filtering Techniques
  5. Regular Security Audits
  • OWASP Top 10 for LLM Applications
  • GDPR AI Guidelines
  • CCPA - Data Rights
  • NIST Cybersecurity Framework
  • ISO 27001: Information Security Management
  • GDPR: General Data Protection Regulation
  • CCPA: California Consumer Privacy Act
  • HIPAA: Health Insurance Portability and Accountability Act