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Télécharger Zip Téléchargement... Métiers associés SOC
Basé sur la classification professionnelle SOC
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"]
🧠 OWASP LLM Security Skill
🎯 Purpose
Enforce LLM-specific security controls aligned with OWASP LLM Top 10 2025.
Key Principle: "LLMs introduce unique security risks requiring specialized controls."
📚 Scope
🔓 Prompt Injection Prevention
💾 Insecure Output Handling
🏋️ Model Denial of Service
�� Supply Chain Vulnerabilities
🔌 Insecure Plugin Design
📊 Excessive Agency
🛡️ Data Leakage Prevention
⚙️ Security Rules
MUST Requirements
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: use_safe_templating_engines
-
csp_headers:
content_security_policy_strict
llm03_training_data_poisoning:
data_quality:
-
source_validation:
verify_training_data_sources
-
adversarial_testing:
test_for_backdoor_triggers
-
data_provenance:
track_dataset_origins
monitoring:
-
model_behavior:
detect_unexpected_outputs
-
regular_retraining:
update_with_clean_datasets
llm04_model_denial_of_service:
rate_limiting:
-
per_user_limits:
100_requests_per_hour
-
cost_caps:
maximum_tokens_per_request
-
timeout_enforcement:
30_second_maximum_response_time
resource_management:
-
queue_management:
priority_queues_for_critical_users
-
circuit_breakers:
auto_disable_on_abuse_detection
llm05_supply_chain_vulnerabilities:
third_party_models:
-
vendor_assessment:
security_evaluation_before_use
-
model_provenance:
verify_official_sources_only
-
sbom:
software_bill_of_materials_for_ai_components
monitoring:
-
dependency_scanning:
check_for_vulnerable_libraries
-
model_updates:
track_security_patches_from_vendors
llm06_sensitive_info_disclosure:
data_protection:
-
no_pii_training:
never_train_on_personal_data
-
output_filtering:
redact_potential_secrets_in_responses
-
context_limits:
limit_context_window_to_reduce_leakage
testing:
-
red_team:
attempt_to_extract_training_data
-
regression_tests:
verify_no_memorization_of_secrets
llm07_insecure_plugin_design:
plugin_security:
-
least_privilege:
plugins_minimal_permissions_required
-
input_validation:
validate_all_plugin_inputs
-
authentication:
require_auth_for_plugin_execution
review_process:
-
security_review:
all_plugins_security_audited
-
sandboxing:
isolate_plugin_execution_environment
llm08_excessive_agency:
authorization:
-
human_approval:
require_approval_for_critical_actions
-
scope_limits:
restrict_llm_to_read_only_operations
-
audit_trail:
log_all_llm_initiated_actions
safeguards:
-
action_validation:
confirm_intended_action_before_execution
-
rollback_capability:
undo_mechanism_for_llm_actions
llm09_overreliance:
human_oversight:
-
fact_checking:
verify_llm_outputs_before_trust
-
disclaimer:
inform_users_llm_may_hallucinate
-
critical_decisions:
never_fully_automate_without_review
llm10_model_theft:
access_controls:
-
api_authentication:
require_strong_auth_for_model_access
-
rate_limiting:
prevent_model_extraction_via_queries
-
watermarking:
embed_watermarks_in_model_outputs
MUST NOT Prohibitions 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
💡 Examples
Example 1: Prompt Injection Prevention
import re
from typing import Optional
def sanitize_user_input (user_input: str ) -> str :
"""Remove potential prompt injection attempts"""
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)
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"""
sanitized_input = sanitize_user_input(user_question)
if not sanitized_input:
return "Invalid input detected"
system_prompt = "You are a helpful assistant. Answer factually and concisely."
user_prompt = f"User question: {sanitized_input} "
response = query_llm(
system=system_prompt,
user=user_prompt,
max_tokens=150 ,
temperature=0.7
)
if contains_suspicious_content(response):
log_security_event("Suspicious LLM output detected" , response)
return "Response blocked for security reasons"
return html_escape(response)
Example 2: Rate Limiting and DoS Prevention
from flask import Flask, request, jsonify
from flask_limiter import Limiter
from flask_limiter.util import get_remote_address
import time
app = Flask(__name__)
limiter = Limiter(
app=app,
key_func=get_remote_address,
default_limits=["100 per hour" , "10 per minute" ],
storage_uri="redis://localhost:6379"
)
MAX_TOKENS_PER_REQUEST = 1000
MAX_COST_PER_USER_PER_DAY = 100
@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' , '' )
estimated_tokens = len (user_input.split()) * 1.3
if estimated_tokens > MAX_TOKENS_PER_REQUEST:
return jsonify({
"error" : "Request exceeds maximum token limit"
}), 400
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
start_time = time.time()
timeout_seconds = 30
try :
response = query_llm_with_timeout(
user_input,
timeout=timeout_seconds
)
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
Example 3: Sensitive Data Leakage Prevention
import re
from typing import List , Tuple
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"""
sensitive_findings = detect_sensitive_data(llm_response)
if sensitive_findings:
log_security_event(
"Sensitive data detected in LLM output" ,
findings=sensitive_findings
)
safe_response = redact_sensitive_data(llm_response)
return {
"response" : safe_response,
"warning" : "Some content was redacted for security" ,
"redacted_count" : len (sensitive_findings)
}
return {
"response" : html_escape(llm_response),
"warning" : None
}
🔗 Integration Skills: ai-governance, secure-development, data-classification
Frameworks: OWASP LLM Top 10 2025, ISO 27001 A.14
📋 Document Control
Version: 1.0 | Updated: 2026-02-10License: Apache-2.0Classification: 
