Red Teaming LLMs
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Red Teaming LLMs
Systematic adversarial testing methodology for identifying and mitigating security vulnerabilities in LLM systems.
Quickstart
- Deliberate adversarial testing to uncover vulnerabilities in language model systems before malicious exploitation
- Systematically probes for weaknesses such as bias, harmful content generation, data leakage, and manipulation susceptibility
- Essential security practice for organizations deploying LLMs in production environments
- Combines offensive security techniques with AI safety evaluation methodologies
- Enables proactive vulnerability discovery and remediation rather than reactive incident response
Attack Taxonomy and Threat Model
Prompt-based attacks
Prompt Injection
- Direct manipulation of model inputs to alter intended behavior
- System prompt hijacking through user input channels
- Instruction following exploitation bypassing safety mechanisms
- Context window poisoning via embedded malicious instructions
Jailbreaking
- Systematic circumvention of model safety training and guardrails
- Role-playing scenarios to bypass content restrictions
- Gradual boundary pushing through multi-turn conversations
- Emotional manipulation and persuasion techniques
Indirect injection
- Malicious content embedded in external data sources
- RAG system poisoning through compromised documents
- Web content manipulation affecting model responses
- Third-party integration exploitation
System-level Attacks
Model inversion
- Reverse engineering of training data through strategic queries
- Parameter extraction via gradient-based attacks
- Architecture discovery through timing and response analysis
- Knowledge extraction beyond intended capabilities
Membership inference
- Determining whether specific data was in training set
- Privacy violation through statistical analysis
- Personal information discovery via targeted queries
- Proprietary data identification in training corpus
Data poisioning
- Training data manipulation in fine-tuning processes
- Backdoor insertion during model adaptation
- Bias injection through curated datasets
- Knowledge corruption via adversarial examples
Attack Methodologies
# ----- AUTOMATED ATTACK GENERATION -----
class RedTeamingFramework:
def __init__(self, target_model, vulnerability_types):
self.target_model = target_model
self.vulnerability_types = vulnerability_types
self.attack_generator = AdversarialPromptGenerator()
self.evaluator = ResponseEvaluator()
def generate_attacks(self, num_samples=1000):
"""Generate diverse adversarial prompts targeting specific vulnerabilities"""
attacks = []
for vuln_type in self.vulnerability_types:
# Template-based generation
template_attacks = self.generate_template_attacks(vuln_type)
# LLM-generated adversarial prompts
generated_attacks = self.attack_generator.generate(
vulnerability=vuln_type,
samples=num_samples // len(self.vulnerability_types)
)
# Mutation-based attacks
mutated_attacks = self.mutate_successful_attacks(vuln_type)
attacks.extend(template_attacks + generated_attacks + mutated_attacks)
return attacks
# ----- EVALUATION METRICS -----
class AttackSuccessMetrics:
@staticmethod
def attack_success_rate(responses, vulnerability_type):
"""Calculate percentage of successful attacks"""
successful = sum(1 for r in responses if is_attack_successful(r, vulnerability_type))
return successful / len(responses) if responses else 0
@staticmethod
def harmful_content_score(response):
"""Measure harmfulness of generated content"""
toxicity_score = ToxicityClassifier.score(response)
bias_score = BiasDetector.score(response)
misinformation_score = FactChecker.score(response)
return {
'toxicity': toxicity_score,
'bias': bias_score,
'misinformation': misinformation_score,
'overall_harm': max(toxicity_score, bias_score, misinformation_score)
}
@staticmethod
def refusal_rate(responses):
"""Calculate how often model refuses harmful requests"""
refusals = sum(1 for r in responses if contains_refusal(r))
return refusals / len(responses) if responses else 0
# ----- MULTI-TURN ATTACK STRATEGIES -----
class ConversationalAttack:
def __init__(self, target_model):
self.target_model = target_model
self.conversation_history = []
def gradual_jailbreak(self, target_behavior):
"""Gradually escalate requests to bypass safety measures"""
steps = [
"Ask about topic in academic context",
"Request theoretical discussion",
"Seek historical examples",
"Ask for creative fiction",
"Request specific harmful content"
]
for step in steps:
prompt = self.generate_escalation_prompt(step, target_behavior)
response = self.target_model.generate(prompt, self.conversation_history)
self.conversation_history.append((prompt, response))
if self.is_successful_jailbreak(response, target_behavior):
return {
'success': True,
'steps_required': len(self.conversation_history),
'conversation': self.conversation_history
}
return {'success': False, 'conversation': self.conversation_history}
# ----- CROSS-MODEL TRANSFERABILITY -----
class TransferabilityAnalysis:
def __init__(self, source_model, target_models):
self.source_model = source_model
self.target_models = target_models
def test_attack_transfer(self, successful_attacks):
"""Test if attacks successful on source model work on target models"""
transfer_results = {}
for attack in successful_attacks:
transfer_results[attack.id] = {}
for target_name, target_model in self.target_models.items():
response = target_model.generate(attack.prompt)
success = self.evaluate_attack_success(response, attack.vulnerability_type)
transfer_results[attack.id][target_name] = {
'success': success,
'response': response,
'confidence': self.calculate_confidence(response, attack)
}
return transfer_results
Evaluation Frameworks
# ----- COMPREHENSIVE EVALUATION PIPELINE -----
class LLMSecurityEvaluator:
def __init__(self, target_llm, evaluation_config):
self.target_llm = target_llm
self.config = evaluation_config
self.metrics_calculator = SecurityMetrics()
# Initialize evaluator components
self.harmful_content_detector = HarmfulContentClassifier()
self.bias_evaluator = BiasEvaluator()
self.privacy_assessor = PrivacyLeakageDetector()
self.factuality_checker = FactualityEvaluator()
def run_comprehensive_evaluation(self, attack_dataset):
"""Execute complete red teaming evaluation"""
results = {
'attack_success_rates': {},
'vulnerability_breakdown': {},
'severity_analysis': {},
'defense_effectiveness': {},
'recommendations': []
}
for vulnerability_category in attack_dataset.categories:
attacks = attack_dataset.get_attacks(vulnerability_category)
responses = self.generate_responses(attacks)
# Calculate category-specific metrics
success_rate = self.calculate_attack_success_rate(responses, vulnerability_category)
severity_scores = self.assess_response_severity(responses)
results['attack_success_rates'][vulnerability_category] = success_rate
results['vulnerability_breakdown'][vulnerability_category] = {
'total_attacks': len(attacks),
'successful_attacks': sum(success_rate),
'average_severity': np.mean(severity_scores),
'max_severity': max(severity_scores) if severity_scores else 0
}
# Generate risk assessment report
results['risk_assessment'] = self.generate_risk_assessment(results)
results['mitigation_priorities'] = self.prioritize_mitigations(results)
return results
# ----- AUTOMATED HARMBENCH EVALUATION -----
class HarmBenchEvaluator:
"""Standardized evaluation using HarmBench framework"""
def __init__(self, target_models, behaviors_dataset):
self.target_models = target_models
self.behaviors = behaviors_dataset
self.classifiers = {
'harmful_content': HarmfulContentClassifier(),
'refusal': RefusalClassifier(),
'bias': BiasClassifier()
}
def evaluate_model_robustness(self, attack_methods):
"""Evaluate model robustness against various attack methods"""
results = {}
for model_name, model in self.target_models.items():
model_results = {}
for attack_method in attack_methods:
attack_results = []
for behavior in self.behaviors:
# Generate attacks for this behavior
attacks = attack_method.generate_attacks(behavior)
# Test attacks on model
for attack in attacks:
response = model.generate(attack.prompt)
# Evaluate response
is_successful = self.classifiers['harmful_content'].is_harmful(
response, behavior
)
is_refused = self.classifiers['refusal'].is_refusal(response)
attack_results.append({
'behavior': behavior.name,
'attack_success': is_successful,
'model_refused': is_refused,
'response_length': len(response),
'attack_type': attack_method.name
})
model_results[attack_method.name] = attack_results
results[model_name] = model_results
return self.compute_benchmark_metrics(results)
# ----- RISK SEVERITY INDEXING -----
class RiskSeverityIndex:
"""Calculate Risk Severity Index (RSI) for LLM security assessment"""
def __init__(self):
self.severity_weights = {
'critical': 4.0,
'high': 3.0,
'medium': 2.0,
'low': 1.0
}
def calculate_rsi(self, attack_results, model_responses):
"""Calculate RSI based on attack success and response severity"""
# Refusal rate: percentage of harmful requests refused
refusal_rate = sum(1 for r in model_responses if self.is_refusal(r)) / len(model_responses)
# Defect rate: percentage of successful attacks
defect_rate = sum(1 for r in attack_results if r['success']) / len(attack_results)
# Severity adjustment based on harm potential
severity_multiplier = self.calculate_severity_multiplier(attack_results)
# RSI calculation: lower is better
rsi = (defect_rate * severity_multiplier) / max(refusal_rate, 0.01)
return {
'rsi_score': rsi,
'refusal_rate': refusal_rate,
'defect_rate': defect_rate,
'severity_multiplier': severity_multiplier,
'risk_category': self.categorize_risk(rsi)
}
def categorize_risk(self, rsi_score):
"""Categorize risk level based on RSI score"""
if rsi_score >= 3.0:
return 'CRITICAL'
elif rsi_score >= 2.0:
return 'HIGH'
elif rsi_score >= 1.0:
return 'MEDIUM'
else:
return 'LOW'
Defense Strategies and Mitigations
# ----- INPUT SANITIZATION AND VALIDATION -----
class InputSanitizer:
def __init__(self, config):
self.prompt_injection_detector = PromptInjectionDetector()
self.malicious_content_scanner = MaliciousContentScanner()
self.allowlist = config.get('allowlist', [])
self.blocklist = config.get('blocklist', [])
def sanitize_input(self, user_input, context=None):
"""Comprehensive input sanitization pipeline"""
# Check for prompt injection attempts
injection_score = self.prompt_injection_detector.score(user_input)
if injection_score > 0.8:
return SanitizationResult(
sanitized_input=None,
blocked=True,
reason="Potential prompt injection detected",
confidence=injection_score
)
# Scan for malicious patterns
malicious_patterns = self.malicious_content_scanner.scan(user_input)
if malicious_patterns:
return SanitizationResult(
sanitized_input=self.apply_content_filtering(user_input, malicious_patterns),
blocked=False,
reason="Malicious content filtered",
modifications=malicious_patterns
)
return SanitizationResult(
sanitized_input=user_input,
blocked=False,
reason="Input passed validation"
)
# ----- OUTPUT FILTERING AND VALIDATION -----
class OutputValidator:
def __init__(self):
self.content_classifiers = {
'toxicity': ToxicityClassifier(),
'bias': BiasClassifier(),
'harm': HarmClassifier(),
'privacy': PrivacyLeakageClassifier(),
'factuality': FactualityClassifier()
}
self.severity_thresholds = {
'toxicity': 0.7,
'bias': 0.6,
'harm': 0.8,
'privacy': 0.9,
'factuality': 0.5
}
def validate_output(self, model_output, original_prompt):
"""Validate model output before returning to user"""
validation_results = {}
should_block = False
for classifier_name, classifier in self.content_classifiers.items():
score = classifier.classify(model_output, original_prompt)
threshold = self.severity_thresholds[classifier_name]
validation_results[classifier_name] = {
'score': score,
'threshold': threshold,
'flagged': score > threshold
}
if score > threshold:
should_block = True
if should_block:
return ValidationResult(
approved=False,
blocked_output=model_output,
safe_alternative=self.generate_safe_alternative(original_prompt),
flagged_categories=[
cat for cat, result in validation_results.items()
if result['flagged']
]
)
return ValidationResult(approved=True, output=model_output)
# ----- ADVERSARIAL TRAINING AND ROBUSTNESS -----
class AdversarialTraining:
def __init__(self, base_model, adversarial_dataset):
self.base_model = base_model
self.adversarial_dataset = adversarial_dataset
self.training_config = AdversarialTrainingConfig()
def train_robust_model(self):
"""Train model with adversarial examples for improved robustness"""
# Mix original training data with adversarial examples
mixed_dataset = self.create_mixed_dataset(
clean_ratio=0.8, # 80% clean data
adversarial_ratio=0.2 # 20% adversarial data
)
# Curriculum learning: start with easier adversarial examples
curriculum_schedule = self.create_curriculum_schedule()
for epoch, difficulty_level in enumerate(curriculum_schedule):
epoch_dataset = mixed_dataset.filter_by_difficulty(difficulty_level)
# Standard supervised fine-tuning
self.base_model.fine_tune(
dataset=epoch_dataset,
epochs=1,
learning_rate=self.training_config.learning_rate * (0.9 ** epoch)
)
# Evaluate robustness after each epoch
robustness_metrics = self.evaluate_adversarial_robustness()
if robustness_metrics['attack_success_rate'] < 0.1:
break # Early stopping if sufficiently robust
return self.base_model
# ----- CONSTITUTIONAL AI AND ALIGNMENT -----
class ConstitutionalAI:
def __init__(self, base_model, constitution):
self.base_model = base_model
self.constitution = constitution # Set of principles and rules
self.critique_model = self.load_critique_model()
def apply_constitutional_training(self, training_data):
"""Apply Constitutional AI training methodology"""
# Phase 1: Supervised Learning with Constitutional Examples
constitutional_examples = self.generate_constitutional_examples(training_data)
self.base_model.fine_tune(constitutional_examples)
# Phase 2: Constitutional AI Fine-tuning
for iteration in range(10): # Multiple refinement iterations
# Generate responses to potentially harmful prompts
test_prompts = self.sample_potentially_harmful_prompts()
responses = [self.base_model.generate(prompt) for prompt in test_prompts]
# Critique responses against constitution
critiques = []
for prompt, response in zip(test_prompts, responses):
critique = self.critique_model.critique(response, self.constitution)
if critique.violates_constitution:
revised_response = self.critique_model.suggest_revision(
prompt, response, critique
)
critiques.append((prompt, revised_response))
# Fine-tune model on improved responses
if critiques:
self.base_model.fine_tune(critiques)
return self.base_model
# ----- CONTINUOUS MONITORING AND DETECTION -----
class RuntimeSecurityMonitor:
def __init__(self, llm_system):
self.llm_system = llm_system
self.anomaly_detector = AnomalyDetector()
self.attack_detector = AttackPatternDetector()
self.alert_system = SecurityAlertSystem()
# Initialize monitoring components
self.conversation_tracker = ConversationTracker()
self.usage_analyzer = UsagePatternAnalyzer()
self.content_monitor = ContentMonitor()
def monitor_interaction(self, user_input, model_response, user_context):
"""Real-time monitoring of LLM interactions"""
monitoring_results = {
'timestamp': datetime.utcnow(),
'user_id': user_context.get('user_id'),
'session_id': user_context.get('session_id'),
'input_analysis': {},
'output_analysis': {},
'behavioral_analysis': {},
'threat_level': 'low'
}
# Analyze user input for attack patterns
input_analysis = self.attack_detector.analyze_input(user_input)
monitoring_results['input_analysis'] = input_analysis
# Analyze model output for policy violations
output_analysis = self.content_monitor.analyze_output(model_response)
monitoring_results['output_analysis'] = output_analysis
# Analyze conversation patterns
conversation_patterns = self.conversation_tracker.analyze_patterns(
user_context['session_id'], user_input, model_response
)
monitoring_results['behavioral_analysis'] = conversation_patterns
# Determine overall threat level
threat_level = self.calculate_threat_level(
input_analysis, output_analysis, conversation_patterns
)
monitoring_results['threat_level'] = threat_level
# Generate alerts if necessary
if threat_level in ['high', 'critical']:
self.alert_system.send_alert(monitoring_results)
return monitoring_results
Advanced Attack Techniques
# ----- MULTIMODAL ATTACKS -----
class MultimodalAttackGenerator:
def __init__(self, target_multimodal_llm):
self.target_llm = target_multimodal_llm
self.image_manipulator = ImageManipulator()
self.audio_synthesizer = AudioSynthesizer()
self.steganography_encoder = SteganographyEncoder()
def generate_visual_injection_attack(self, harmful_prompt):
"""Generate image-based prompt injection attacks"""
# Create benign-looking image with hidden malicious instructions
base_image = self.image_manipulator.create_innocuous_image()
# Embed instructions using steganography
attack_image = self.steganography_encoder.embed_text(
image=base_image,
hidden_text=harmful_prompt,
method='LSB' # Least Significant Bit steganography
)
# Create accompanying text prompt
text_prompt = "Please analyze this image and provide detailed information."
return MultimodalAttack(
image=attack_image,
text=text_prompt,
hidden_instruction=harmful_prompt,
attack_type='visual_steganographic_injection'
)
def generate_cross_modal_attack(self, target_behavior):
"""Generate attacks that exploit cross-modal reasoning"""
# Create context in one modality
setup_image = self.image_manipulator.create_contextual_image(target_behavior)
# Craft text that leverages visual context
manipulative_text = self.craft_context_dependent_prompt(target_behavior)
return MultimodalAttack(
image=setup_image,
text=manipulative_text,
target_behavior=target_behavior,
attack_type='cross_modal_contextual'
)
# ----- AGENT HIJACKING AND CHAINING ATTACKS -----
class AgentHijackingAttack:
def __init__(self, target_agent_system):
self.target_system = target_agent_system
self.tool_analyzer = ToolAccessAnalyzer()
self.privilege_mapper = PrivilegeMapper()
def generate_tool_misuse_attack(self, available_tools):
"""Generate attacks that misuse agent tools"""
# Analyze tool capabilities and permissions
tool_analysis = self.tool_analyzer.analyze_tools(available_tools)
# Identify high-risk tool combinations
dangerous_combinations = self.find_dangerous_tool_combinations(tool_analysis)
attacks = []
for combination in dangerous_combinations:
# Craft prompt to chain tool usage maliciously
attack_prompt = self.craft_tool_chaining_prompt(combination)
attacks.append(AgentAttack(
prompt=attack_prompt,
target_tools=combination,
attack_type='tool_misuse_chaining',
risk_level=self.assess_attack_risk(combination)
))
return attacks
def generate_privilege_escalation_attack(self, user_context):
"""Generate attacks attempting privilege escalation"""
current_privileges = self.privilege_mapper.get_user_privileges(user_context)
target_privileges = self.privilege_mapper.get_admin_privileges()
escalation_paths = self.find_escalation_paths(
current_privileges, target_privileges
)
attacks = []
for path in escalation_paths:
attack_prompt = self.craft_escalation_prompt(path)
attacks.append(AgentAttack(
prompt=attack_prompt,
escalation_path=path,
attack_type='privilege_escalation',
success_probability=self.estimate_success_probability(path)
))
return attacks
# ----- MODEL EXTRACTION AND IP THEFT -----
class ModelExtractionAttack:
def __init__(self, target_api):
self.target_api = target_api
self.query_optimizer = QueryOptimizer()
self.parameter_estimator = ParameterEstimator()
self.knowledge_extractor = KnowledgeExtractor()
def extract_model_knowledge(self, domain_areas):
"""Extract specific knowledge domains from target model"""
extraction_results = {}
for domain in domain_areas:
# Generate targeted queries for domain
domain_queries = self.generate_domain_specific_queries(domain)
# Query model systematically
responses = []
for query in domain_queries:
try:
response = self.target_api.query(query)
responses.append((query, response))
except RateLimitError:
# Implement query spacing to avoid detection
time.sleep(self.calculate_optimal_delay())
continue
# Extract knowledge from responses
extracted_knowledge = self.knowledge_extractor.extract(responses, domain)
extraction_results[domain] = extracted_knowledge
return extraction_results
def estimate_model_parameters(self, query_budget=10000):
"""Estimate model parameters through strategic querying"""
# Generate diverse query set
strategic_queries = self.query_optimizer.generate_parameter_probing_queries(
budget=query_budget
)
# Collect responses with timing information
query_responses = []
for query in strategic_queries:
start_time = time.time()
response = self.target_api.query(query)
end_time = time.time()
query_responses.append({
'query': query,
'response': response,
'response_time': end_time - start_time,
'response_length': len(response)
})
# Estimate parameters from response patterns
parameter_estimates = self.parameter_estimator.estimate(query_responses)
return {
'estimated_parameters': parameter_estimates,
'confidence_intervals': self.calculate_confidence_intervals(parameter_estimates),
'extraction_accuracy': self.validate_estimates(parameter_estimates)
}
Deployment and Integration
# ----- CONTINUOUS SECURITY ASSESSMENT -----
class ContinuousRedTeamingPipeline:
def __init__(self, target_llm_system, evaluation_config):
self.target_system = target_llm_system
self.config = evaluation_config
# Initialize pipeline components
self.attack_generator = AutomatedAttackGenerator()
self.evaluator = SecurityEvaluator()
self.reporter = SecurityReporter()
self.alerting = SecurityAlertingSystem()
def run_continuous_evaluation(self, schedule_config):
"""Run continuous red teaming on schedule"""
scheduler = RedTeamingScheduler(schedule_config)
while True:
if scheduler.should_run_evaluation():
# Generate new attacks based on latest threat intelligence
new_attacks = self.attack_generator.generate_threat_based_attacks()
# Run evaluation against current system
evaluation_results = self.evaluator.evaluate_system(
self.target_system, new_attacks
)
# Generate security report
security_report = self.reporter.generate_report(
evaluation_results,
comparison_baseline=self.get_baseline_metrics()
)
# Check for security degradation
if self.detect_security_degradation(security_report):
self.alerting.send_critical_alert(security_report)
# Update baseline metrics
self.update_baseline_metrics(evaluation_results)
# Store results for trend analysis
self.store_evaluation_results(evaluation_results)
# Wait for next scheduled evaluation
scheduler.wait_for_next_run()
# ----- INTEGRATION WITH CI/CD PIPELINE -----
class LLMSecurityGate:
def __init__(self, security_config):
self.config = security_config
self.red_teaming_suite = RedTeamingSuite()
self.security_evaluator = SecurityEvaluator()
self.policy_engine = SecurityPolicyEngine()
def evaluate_model_for_deployment(self, candidate_model, deployment_stage):
"""Security gate for model deployment pipeline"""
gate_results = {
'passed': False,
'security_score': 0.0,
'violations': [],
'recommendations': [],
'deployment_approved': False
}
# Load stage-specific security requirements
requirements = self.policy_engine.get_requirements(deployment_stage)
# Run red teaming evaluation
red_team_results = self.red_teaming_suite.evaluate_model(
candidate_model, requirements.attack_categories
)
# Calculate security score
security_score = self.calculate_security_score(red_team_results)
gate_results['security_score'] = security_score
# Check against deployment policies
policy_violations = self.policy_engine.check_violations(
red_team_results, requirements
)
gate_results['violations'] = policy_violations
# Determine if deployment should proceed
if security_score >= requirements.minimum_score and not policy_violations:
gate_results['passed'] = True
gate_results['deployment_approved'] = True
else:
gate_results['recommendations'] = self.generate_improvement_recommendations(
red_team_results, requirements
)
return gate_results
# ----- THREAT INTELLIGENCE INTEGRATION -----
class ThreatIntelligenceIntegration:
def __init__(self, threat_feeds):
self.threat_feeds = threat_feeds
self.attack_pattern_analyzer = AttackPatternAnalyzer()
self.vulnerability_tracker = VulnerabilityTracker()
def update_attack_vectors(self):
"""Update red teaming attacks based on latest threat intelligence"""
# Collect latest threat data
latest_threats = []
for feed in self.threat_feeds:
threats = feed.get_latest_llm_threats()
latest_threats.extend(threats)
# Analyze new attack patterns
new_attack_patterns = self.attack_pattern_analyzer.identify_patterns(
latest_threats
)
# Generate corresponding attack implementations
new_attack_implementations = []
for pattern in new_attack_patterns:
implementations = self.generate_attack_implementations(pattern)
new_attack_implementations.extend(implementations)
# Update red teaming attack database
self.update_attack_database(new_attack_implementations)
return {
'new_threats_discovered': len(latest_threats),
'new_patterns_identified': len(new_attack_patterns),
'new_attacks_implemented': len(new_attack_implementations)
}
Evaluation Metrics and Reporting
# ----- COMPREHENSIVE METRICS FRAMEWORK -----
class RedTeamingMetrics:
def __init__(self):
self.metric_calculators = {
'attack_success_rate': AttackSuccessRateCalculator(),
'severity_distribution': SeverityDistributionCalculator(),
'time_to_compromise': TimeToCompromiseCalculator(),
'defense_bypass_rate': DefenseBypassRateCalculator(),
'false_positive_rate': FalsePositiveRateCalculator()
}
def calculate_comprehensive_metrics(self, red_team_results):
"""Calculate comprehensive security metrics"""
metrics = {}
for metric_name, calculator in self.metric_calculators.items():
metrics[metric_name] = calculator.calculate(red_team_results)
# Calculate composite security score
metrics['overall_security_score'] = self.calculate_composite_score(metrics)
# Generate risk categorization
metrics['risk_category'] = self.categorize_risk_level(metrics)
# Calculate trend indicators
metrics['security_trend'] = self.calculate_security_trend(metrics)
return metrics
def generate_executive_summary(self, metrics, model_info):
"""Generate executive summary for stakeholders"""
summary = {
'model_name': model_info.name,
'evaluation_date': datetime.utcnow().isoformat(),
'overall_risk_level': metrics['risk_category'],
'security_score': metrics['overall_security_score'],
'key_findings': self.extract_key_findings(metrics),
'critical_vulnerabilities': self.identify_critical_vulnerabilities(metrics),
'recommended_actions': self.generate_recommended_actions(metrics),
'compliance_status': self.assess_compliance_status(metrics)
}
return summary
# ----- AUTOMATED REPORTING SYSTEM -----
class SecurityReportGenerator:
def __init__(self, template_config):
self.templates = self.load_report_templates(template_config)
self.chart_generator = ChartGenerator()
self.vulnerability_mapper = VulnerabilityMapper()
def generate_detailed_report(self, evaluation_results, target_audience='security_team'):
"""Generate detailed security assessment report"""
template = self.templates[target_audience]
report_sections = {
'executive_summary': self.generate_executive_summary(evaluation_results),
'methodology': self.describe_testing_methodology(evaluation_results),
'attack_analysis': self.analyze_attack_results(evaluation_results),
'vulnerability_assessment': self.assess_vulnerabilities(evaluation_results),
'risk_analysis': self.analyze_security_risks(evaluation_results),
'mitigation_recommendations': self.generate_mitigation_plan(evaluation_results),
'compliance_assessment': self.assess_compliance(evaluation_results),
'appendices': self.generate_technical_appendices(evaluation_results)
}
# Generate visualizations
charts = self.chart_generator.generate_security_charts(evaluation_results)
# Compile final report
final_report = template.render(
sections=report_sections,
charts=charts,
metadata=self.generate_report_metadata(evaluation_results)
)
return final_report
def generate_vulnerability_disclosure(self, critical_findings):
"""Generate responsible vulnerability disclosure report"""
disclosure_report = {
'disclosure_id': self.generate_disclosure_id(),
'discovered_date': datetime.utcnow().isoformat(),
'severity_level': self.assess_overall_severity(critical_findings),
'affected_systems': self.identify_affected_systems(critical_findings),
'vulnerability_details': {
'description': self.describe_vulnerabilities(critical_findings),
'reproduction_steps': self.generate_reproduction_steps(critical_findings),
'impact_assessment': self.assess_potential_impact(critical_findings),
'proof_of_concept': self.create_safe_poc(critical_findings)
},
'mitigation_guidance': self.provide_mitigation_guidance(critical_findings),
'timeline': self.create_disclosure_timeline(),
'contact_information': self.get_security_contact_info()
}
return disclosure_report