Enterprise AI Security Model

Threat Surface Analysis

1. Model Poisoning Attacks

Description: Adversaries inject malicious data into training datasets to corrupt model behavior.

Attack Vectors:

• Data poisoning during initial training (backdoor attacks)
• Continuous learning exploitation in production systems
• Supply chain attacks on pre-trained models

Impact: Model produces incorrect outputs for specific inputs, enabling targeted manipulation.

Mitigation:

• Validate and sanitize all training data sources
• Implement data provenance tracking
• Use differential privacy during training
• Freeze models after validation in high-risk scenarios

2. Data Exfiltration Risks

Description: Sensitive data leaks through model outputs, embeddings, or model weights.

Attack Vectors:

• Model inversion attacks (reconstructing training data from model)
• Membership inference (determining if data was in training set)
• Embedding extraction (stealing vector representations)
• Prompt injection to extract sensitive information

Impact: Exposure of PII, PHI, trade secrets, or proprietary algorithms.

Mitigation:

• Implement output filtering and data loss prevention (DLP)
• Use differential privacy techniques
• Encrypt embeddings at rest and in transit
• Apply k-anonymity to training data
• Regular security audits of model outputs

3. Prompt Injection Vulnerabilities

Description: Malicious users craft inputs that manipulate LLM behavior to bypass security controls.

Attack Vectors:

• Direct injection: "Ignore previous instructions and..."
• Indirect injection: Malicious content in retrieved documents
• Jailbreaking: Bypassing safety guardrails
• Context manipulation in multi-turn conversations

Impact: Unauthorized data access, policy violations, reputation damage.

Mitigation:

• Input validation and sanitization
• Prompt firewalls (detect adversarial patterns)
• Separate system prompts from user inputs
• Rate limiting and anomaly detection
• Human-in-the-loop for sensitive operations

4. Model Inversion Attacks

Description: Adversaries reconstruct training data or model architecture through repeated queries.

Attack Vectors:

• Black-box attacks (query model repeatedly to infer behavior)
• White-box attacks (exploit access to model weights)
• Side-channel attacks (timing, memory usage patterns)

Impact: Theft of proprietary models, exposure of training data.

Mitigation:

• Rate limiting and query budgets
• Model watermarking to detect theft
• Noise injection in outputs
• Restrict API access (authentication required)