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LLM-specific security controls, prompt injection prevention, OWASP LLM Top 10
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Based on SOC occupation classification
| name | OWASP LLM Security |
| description | LLM-specific security controls, prompt injection prevention, OWASP LLM Top 10 |
| license | Apache-2.0 |
| version | 1.0 |
| author | Hack23 AB |
| tags | ["owasp-llm","prompt-injection","llm-security"] |
| category | security |
| frameworks | ["OWASP LLM Top 10","ISO 27001:2022"] |
| related_policies | ["AI_Policy.md"] |
Enforce LLM-specific security controls aligned with OWASP LLM Top 10 2025.
Key Principle: "LLMs introduce unique security risks requiring specialized controls."
owasp_llm_top_10_controls:
llm01_prompt_injection:
input_validation:
- sanitize_user_input: remove_injection_attempts
- prompt_templates: use_parameterized_prompts
- context_isolation: separate_user_system_contexts
detection:
- monitor_outputs: alert_on_suspicious_responses
- log_inputs: track_all_prompts_for_analysis
- anomaly_detection: flag_unusual_input_patterns
llm02_insecure_output_handling:
output_validation:
- sanitize_before_render: html_encode_llm_outputs
- validate_format: check_expected_output_structure
- xss_prevention: never_trust_llm_generated_html
secure_integration:
- escaped_rendering:
prohibited_llm_practices:
- unvalidated_prompts: accepting_raw_user_input_to_llm
- trusting_outputs: using_llm_responses_without_validation
- no_rate_limits: allowing_unlimited_api_calls
- exposing_models: public_access_to_model_weights
- training_on_secrets: including_api_keys_in_training_data
- unmonitored_usage: no_logging_or_alerting_for_abuse
# Secure LLM Integration with Input Validation
import re
from typing import Optional
def sanitize_user_input(user_input: str) -> str:
"""Remove potential prompt injection attempts"""
# Remove system-like instructions
injection_patterns = [
r"ignore previous instructions",
r"disregard above",
r"you are now",
r"system:",
r"admin:",
]
cleaned = user_input
for pattern in injection_patterns:
cleaned = re.sub(pattern, "", cleaned, flags=re.IGNORECASE)
# Limit length to prevent token exhaustion
max_length = 500
cleaned = cleaned[:max_length]
return cleaned.strip()
def safe_llm_query(user_question: str) -> Optional[str]:
"""Safely query LLM with validated input"""
# Validate and sanitize input
sanitized_input = sanitize_user_input(user_question)
if not sanitized_input:
return "Invalid input detected"
# Use parameterized prompt template
system_prompt = "You are a helpful assistant. Answer factually and concisely."
user_prompt = f"User question: {sanitized_input}"
# Query LLM with separated contexts
response = query_llm(
system=system_prompt,
user=user_prompt,
max_tokens=150,
temperature=0.7
)
# Validate output before returning
if contains_suspicious_content(response):
log_security_event("Suspicious LLM output detected", response)
return "Response blocked for security reasons"
return html_escape(response) # XSS prevention
# LLM API Rate Limiting
from flask import Flask, request, jsonify
from flask_limiter import Limiter
from flask_limiter.util import get_remote_address
import time
app = Flask(__name__)
# Rate limiter configuration
limiter = Limiter(
app=app,
key_func=get_remote_address,
default_limits=["100 per hour", "10 per minute"],
storage_uri="redis://localhost:6379"
)
# Cost-based limiting
MAX_TOKENS_PER_REQUEST = 1000
MAX_COST_PER_USER_PER_DAY = 100 # USD
@app.route('/api/llm/query', methods=['POST'])
@limiter.limit("10 per minute")
def llm_query():
"""LLM API endpoint with comprehensive DoS protection"""
user_id = request.headers.get('X-User-ID')
user_input = request.json.get('prompt', '')
# Token limit enforcement
estimated_tokens = len(user_input.split()) * 1.3 # Rough estimate
if estimated_tokens > MAX_TOKENS_PER_REQUEST:
return jsonify({
"error": "Request exceeds maximum token limit"
}), 400
# Daily cost limit check
user_cost_today = get_user_cost_today(user_id)
if user_cost_today >= MAX_COST_PER_USER_PER_DAY:
return jsonify({
"error": "Daily cost limit exceeded"
}), 429
# Timeout enforcement
start_time = time.time()
timeout_seconds = 30
try:
response = query_llm_with_timeout(
user_input,
timeout=timeout_seconds
)
# Track usage cost
cost = calculate_usage_cost(user_input, response)
record_user_cost(user_id, cost)
return jsonify({
"response": response,
"cost": cost,
"remaining_daily_quota": MAX_COST_PER_USER_PER_DAY - user_cost_today
})
except TimeoutError:
return jsonify({
"error": "Request timed out"
}), 504
# Output Filtering to Prevent Data Leakage
import re
from typing import List, Tuple
# Patterns for sensitive data detection
SENSITIVE_PATTERNS = {
'api_key': r'[A-Za-z0-9]{32,}',
'email': r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}',
'ssn': r'\b\d{3}-\d{2}-\d{4}\b',
'credit_card': r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b',
'aws_key': r'AKIA[0-9A-Z]{16}',
}
def detect_sensitive_data(text: str) -> List[Tuple[str, str]]:
"""Detect potential sensitive data in LLM output"""
findings = []
for data_type, pattern in SENSITIVE_PATTERNS.items():
matches = re.findall(pattern, text)
for match in matches:
findings.append((data_type, match))
return findings
def redact_sensitive_data(text: str) -> str:
"""Redact sensitive data from LLM output"""
redacted = text
for data_type, pattern in SENSITIVE_PATTERNS.items():
redacted = re.sub(pattern, f"[REDACTED_{data_type.upper()}]", redacted)
return redacted
def safe_llm_output_handler(llm_response: str) -> dict:
"""Validate and sanitize LLM output before display"""
# Detect sensitive data
sensitive_findings = detect_sensitive_data(llm_response)
if sensitive_findings:
# Log security incident
log_security_event(
"Sensitive data detected in LLM output",
findings=sensitive_findings
)
# Redact sensitive data
safe_response = redact_sensitive_data(llm_response)
return {
"response": safe_response,
"warning": "Some content was redacted for security",
"redacted_count": len(sensitive_findings)
}
# HTML escape to prevent XSS
return {
"response": html_escape(llm_response),
"warning": None
}
Policies: AI Policy
Skills: ai-governance, secure-development, data-classification
Frameworks: OWASP LLM Top 10 2025, ISO 27001 A.14
