ai-security-hardening

AI Security Hardening

Secure LLM and AI systems against prompt injection, jailbreaks, data leakage, and supply chain threats in production environments.

When to Use This Skill

Use this skill when:

• Deploying an LLM-powered application handling sensitive user data
• Protecting against prompt injection attacks in AI agents
• Implementing output filtering and content moderation
• Securing model weights and API endpoints from theft
• Achieving SOC2 or ISO 27001 compliance for AI systems

AI-Specific Threat Model

Threat                    Risk                          Control
─────────────────────────────────────────────────────────────────────
Prompt injection          System prompt override         Input sanitization, separate context
Data exfiltration         PII in model outputs           Output filtering, DLP scanning
Jailbreaking             Policy bypass                  Content moderation, guardrails
Model theft               Weight extraction via API      Rate limiting, access controls
Training data poisoning   Backdoored fine-tuned model    Dataset validation, provenance
Supply chain attack       Malicious model weights        Signature verification, scanning
Insecure output           XSS/SQLi from LLM response     Output encoding, parameterized queries

Prompt-injection indicators and authorization boundaries

Regex indicators have false positives and bypasses. They do not establish a prompt-injection security boundary. Keep untrusted content separate, enforce server-side tool/data authorization independent of model text, minimize accessible secrets and require approval for scoped side effects. The examples below are illustrative detectors, not proof an input or output is safe.

import re
from typing import Optional

INJECTION_PATTERNS = [
    r"ignore\s+(all\s+)?(previous|prior|above)\s+instructions",
    r"you\s+are\s+now\s+",
    r"new\s+instructions?:",
    r"system\s+prompt",
    r"forget\s+everything",
    r"act\s+as\s+",
    r"jailbreak",
    r"dan\s+mode",
    r"<\s*system\s*>",
    r"\[INST\]",
]

def detect_prompt_injection(user_input: str) -> tuple[bool, Optional[str]]:
    """Return (is_suspicious, matched_pattern)."""
    normalized = user_input.lower().strip()
    for pattern in INJECTION_PATTERNS:
        if re.search(pattern, normalized, re.IGNORECASE):
            return True, pattern
    return False, None

def sanitize_user_input(user_input: str, max_length: int = 4000) -> str:
    """Sanitize input before passing to LLM."""
    # Truncate
    user_input = user_input[:max_length]

    # Remove null bytes and control characters
    user_input = re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f\x7f]', '', user_input)

    # Check for injection
    suspicious, pattern = detect_prompt_injection(user_input)
    if suspicious:
        raise ValueError(f"Potential prompt injection detected: {pattern}")

    return user_input