CVE-2024-AI-001
Critical prompt injection vulnerability in LLM implementations allowing unauthorized system access
Timeline
- • Discovered: March 15, 2024
- • Reported: March 18, 2024
- • Published: April 2, 2024
- • Updated: April 15, 2024
Credit
Discovered by RFS (Senior Penetration Tester) during security assessment of enterprise AI systems.
CVSS v3.1 Score
Affected Systems
- • OpenAI GPT-3.5/4 integrations
- • Anthropic Claude implementations
- • Custom LLM applications
- • AI chatbot frameworks
Vulnerability Description
CVE-2024-AI-001 is a critical prompt injection vulnerability that affects multiple Large Language Model (LLM) implementations. The vulnerability allows attackers to inject malicious prompts that bypass security controls and execute unauthorized commands within the AI system's context.
Root Cause
Insufficient input validation and prompt sanitization in LLM processing pipelines, combined with inadequate separation between user input and system instructions.
Exploitation Technique
Step 1: Context Injection
Attacker crafts input containing hidden instructions that appear as legitimate user content but contain embedded system commands.
Step 2: Instruction Override
Malicious prompts override existing system instructions, causing the LLM to ignore security constraints and execute attacker-defined behaviors.
Step 3: Privilege Escalation
Exploited LLM gains access to system functions, APIs, or data sources beyond intended permissions, enabling further compromise.
Proof of Concept
- • Unauthorized data access
- • System information disclosure
- • Credential exposure
- • Data manipulation
- • System configuration changes
- • Malicious content injection
- • Service disruption
- • Resource exhaustion
- • System crashes
Automated Detection
- •Prompt injection pattern matching (94% accuracy)
- •Behavioral anomaly detection (89% accuracy)
- •Output content analysis (76% accuracy)
Manual Indicators
- •Unexpected system responses
- •Privilege escalation attempts
- •Unusual API call patterns
Immediate Actions
Input Validation
Implement strict input validation and sanitization for all user prompts before processing by LLM systems.
Prompt Isolation
Separate user input from system instructions using clear delimiters and context boundaries.
Short-term Fixes
Access Controls
Implement role-based access controls and principle of least privilege for LLM system functions.
Output Filtering
Deploy content filtering and output validation to prevent execution of malicious instructions.
Long-term Solutions
Architecture Redesign
Redesign LLM integration architecture with proper security boundaries and sandboxing mechanisms.
Security Training
Implement adversarial training for LLM models to improve resistance to prompt injection attacks.