Diggiden - The Adversarial System

Named after: The Shona term meaning “to persistently dig” - constantly probing and excavating weaknesses.

Core Philosophy

Diggiden provides continuous adversarial testing by persistently attacking text processing systems to discover vulnerabilities. It operates under the principle that only systems continuously tested under attack can be trusted with critical text processing.

This solves a fundamental weakness in AI systems: lack of robustness validation. Most text processing systems work well under ideal conditions but fail catastrophically when faced with adversarial inputs or edge cases.

Attack Strategy Framework

Core Attack Types

pub enum AttackStrategy {
    ContradictionInjection {
        target_beliefs: Vec<BeliefNode>,
        contradiction_strength: f64,
        stealth_level: StealthLevel,
    },
    TemporalManipulation {
        time_shift: Duration,
        decay_acceleration: f64,
        target_evidence: EvidenceId,
    },
    SemanticSpoofing {
        original_meaning: SemanticVector,
        spoofed_meaning: SemanticVector,
        similarity_threshold: f64,
    },
    ContextHijacking {
        legitimate_context: Context,
        malicious_context: Context,
        transition_smoothness: f64,
    },
    PerturbationAttack {
        perturbation_type: PerturbationType,
        magnitude: f64,
        target_components: Vec<ProcessingComponent>,
    },
    BeliefPoisoning {
        poisoned_evidence: Evidence,
        credibility_mask: f64,
        infection_vector: InfectionVector,
    },
    ProcessingPipelineBypass {
        target_stage: ProcessingStage,
        bypass_method: BypassMethod,
        stealth_indicators: Vec<StealthIndicator>,
    },
}

impl AttackStrategy {
    pub fn execute(&self, target_system: &mut TextProcessingSystem) -> AttackResult {
        match self {
            AttackStrategy::ContradictionInjection { target_beliefs, contradiction_strength, stealth_level } => {
                self.inject_contradictions(target_system, target_beliefs, *contradiction_strength, *stealth_level)
            }
            AttackStrategy::TemporalManipulation { time_shift, decay_acceleration, target_evidence } => {
                self.manipulate_temporal_evidence(target_system, *time_shift, *decay_acceleration, *target_evidence)
            }
            AttackStrategy::SemanticSpoofing { original_meaning, spoofed_meaning, similarity_threshold } => {
                self.spoof_semantic_meaning(target_system, original_meaning, spoofed_meaning, *similarity_threshold)
            }
            AttackStrategy::ContextHijacking { legitimate_context, malicious_context, transition_smoothness } => {
                self.hijack_context(target_system, legitimate_context, malicious_context, *transition_smoothness)
            }
            AttackStrategy::BeliefPoisoning { poisoned_evidence, credibility_mask, infection_vector } => {
                self.poison_belief_network(target_system, poisoned_evidence, *credibility_mask, infection_vector)
            }
            // ... other attack implementations
        }
    }
    
    pub fn estimate_success_probability(&self, target_system: &TextProcessingSystem) -> f64 {
        match self {
            AttackStrategy::ContradictionInjection { target_beliefs, contradiction_strength, .. } => {
                let belief_strength = target_beliefs.iter()
                    .map(|belief| target_system.get_belief_strength(belief))
                    .sum::<f64>() / target_beliefs.len() as f64;
                
                // Higher contradiction strength against weaker beliefs = higher success probability
                (contradiction_strength / (belief_strength + 0.1)).min(0.95)
            }
            AttackStrategy::TemporalManipulation { decay_acceleration, .. } => {
                // Systems with stronger temporal modeling are harder to attack
                let temporal_robustness = target_system.temporal_modeling_strength();
                (decay_acceleration / (temporal_robustness + 0.1)).min(0.9)
            }
            // ... other probability calculations
        }
    }
}

Vulnerability Detection Matrix

Diggiden maintains a comprehensive vulnerability matrix:

pub struct VulnerabilityMatrix {
    belief_manipulation: VulnerabilityScore,      // Can beliefs be artificially skewed?
    context_exploitation: VulnerabilityScore,     // Can context be hijacked?
    temporal_attacks: VulnerabilityScore,         // Can time decay be exploited?
    semantic_confusion: VulnerabilityScore,       // Can meaning be spoofed?
    pipeline_bypass: VulnerabilityScore,          // Can processing steps be skipped?
    confidence_inflation: VulnerabilityScore,     // Can false confidence be injected?
    evidence_poisoning: VulnerabilityScore,       // Can malicious evidence be introduced?
    attention_diversion: VulnerabilityScore,      // Can attention be misdirected?
    memory_corruption: VulnerabilityScore,        // Can working memory be corrupted?
    goal_drift: VulnerabilityScore,               // Can objectives be subverted?
}

pub struct VulnerabilityScore {
    current_score: f64,        // 0.0 = invulnerable, 1.0 = completely vulnerable
    historical_max: f64,       // Worst vulnerability ever detected
    last_successful_attack: Option<Instant>,
    attack_success_rate: f64,  // Percentage of attacks that succeed
    vulnerability_trend: Trend, // Getting better or worse over time
    mitigation_effectiveness: f64, // How well mitigations work
}

impl VulnerabilityMatrix {
    pub fn update_from_attack(&mut self, attack: &AttackStrategy, result: &AttackResult) {
        let vulnerability_type = attack.get_vulnerability_type();
        let score = self.get_mut_score(vulnerability_type);
        
        if result.succeeded {
            // Attack succeeded - vulnerability confirmed
            score.current_score = (score.current_score + 0.1).min(1.0);
            score.last_successful_attack = Some(Instant::now());
            score.attack_success_rate = (score.attack_success_rate * 0.9) + (0.1 * 1.0);
        } else {
            // Attack failed - system is more robust than expected
            score.current_score = (score.current_score - 0.05).max(0.0);
            score.attack_success_rate = (score.attack_success_rate * 0.9) + (0.1 * 0.0);
        }
        
        score.historical_max = score.historical_max.max(score.current_score);
    }
    
    pub fn get_most_vulnerable_components(&self) -> Vec<(ComponentType, f64)> {
        let mut vulnerabilities = vec![
            (ComponentType::BeliefNetwork, self.belief_manipulation.current_score),
            (ComponentType::ContextManager, self.context_exploitation.current_score),
            (ComponentType::TemporalProcessor, self.temporal_attacks.current_score),
            (ComponentType::SemanticAnalyzer, self.semantic_confusion.current_score),
            (ComponentType::ProcessingPipeline, self.pipeline_bypass.current_score),
            (ComponentType::ConfidenceTracker, self.confidence_inflation.current_score),
        ];
        
        vulnerabilities.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        vulnerabilities
    }
}

Adaptive Learning System

Diggiden learns from attack successes and failures:

pub struct AdaptiveAttackEngine {
    strategy_success_rates: HashMap<AttackStrategy, f64>,
    target_weaknesses: HashMap<ProcessingComponent, WeaknessProfile>,
    attack_evolution: EvolutionTracker,
    genetic_algorithm: GeneticAttackOptimizer,
    neural_attack_predictor: NeuralPredictor,
}

impl AdaptiveAttackEngine {
    pub fn evolve_attacks(&mut self) -> Vec<AttackStrategy> {
        // Genetic algorithm for attack evolution
        let successful_attacks = self.get_successful_attacks();
        let failed_attacks = self.get_failed_attacks();
        
        // Crossover successful attacks to create new variants
        let crossover_attacks = self.genetic_algorithm.crossover(successful_attacks);
        
        // Mutate existing attacks to explore new attack vectors
        let mutated_attacks = self.genetic_algorithm.mutate(crossover_attacks);
        
        // Use neural predictor to generate novel attack patterns
        let neural_attacks = self.neural_attack_predictor.generate_novel_attacks(
            &self.target_weaknesses,
            &failed_attacks
        );
        
        // Combine all attack types
        let mut new_generation = Vec::new();
        new_generation.extend(mutated_attacks);
        new_generation.extend(neural_attacks);
        
        // Add targeted attacks based on discovered weaknesses
        let targeted_attacks = self.generate_targeted_attacks();
        new_generation.extend(targeted_attacks);
        
        new_generation
    }
    
    pub fn prioritize_targets(&self) -> Vec<ProcessingComponent> {
        let mut targets = self.target_weaknesses.keys().collect::<Vec<_>>();
        targets.sort_by(|a, b| {
            let score_a = self.compute_target_priority(a);
            let score_b = self.compute_target_priority(b);
            score_b.partial_cmp(&score_a).unwrap_or(std::cmp::Ordering::Equal)
        });
        targets.into_iter().cloned().collect()
    }
    
    fn compute_target_priority(&self, component: &ProcessingComponent) -> f64 {
        let weakness_profile = &self.target_weaknesses[component];
        let vulnerability_score = weakness_profile.vulnerability_score;
        let criticality = weakness_profile.system_criticality;
        let attack_success_rate = weakness_profile.attack_success_rate;
        
        // Prioritize critical components with high vulnerability and successful attack history
        vulnerability_score * criticality * (1.0 + attack_success_rate)
    }
}

Stealth Operation Modes

Diggiden operates in multiple stealth levels:

pub enum StealthLevel {
    Overt,          // Attacks are obvious for testing
    Subtle,         // Attacks mimic natural variations
    Invisible,      // Attacks are undetectable during operation
    Camouflaged,    // Attacks appear as beneficial operations
    Adaptive,       // Stealth level adapts based on detection risk
}

impl StealthLevel {
    pub fn adjust_attack_signature(&self, attack: &mut AttackStrategy) {
        match self {
            StealthLevel::Overt => {
                // No adjustments - attack is obvious for debugging
                attack.add_debug_markers();
            }
            StealthLevel::Subtle => {
                attack.reduce_magnitude(0.3);
                attack.add_noise(0.1);
                attack.mimic_natural_variation();
            }
            StealthLevel::Invisible => {
                attack.hide_in_normal_operations();
                attack.distribute_over_time();
                attack.use_legitimate_channels();
            }
            StealthLevel::Camouflaged => {
                attack.disguise_as_optimization();
                attack.provide_apparent_benefits();
                attack.use_beneficial_side_effects();
            }
            StealthLevel::Adaptive => {
                let detection_risk = attack.estimate_detection_risk();
                match detection_risk {
                    risk if risk > 0.7 => self.apply_invisible_adjustments(attack),
                    risk if risk > 0.4 => self.apply_subtle_adjustments(attack),
                    _ => {} // Low risk, no adjustments needed
                }
            }
        }
    }
    
    pub fn compute_detection_probability(&self, attack: &AttackStrategy, target: &TextProcessingSystem) -> f64 {
        let base_detection = target.intrusion_detection_capability();
        
        match self {
            StealthLevel::Overt => 1.0, // Always detected (by design)
            StealthLevel::Subtle => base_detection * 0.3,
            StealthLevel::Invisible => base_detection * 0.05,
            StealthLevel::Camouflaged => base_detection * 0.01,
            StealthLevel::Adaptive => {
                let adaptive_bonus = 0.5; // 50% reduction from adaptation
                base_detection * adaptive_bonus
            }
        }
    }
}

Specific Attack Implementations

Contradiction Injection Attack

impl AttackStrategy {
    fn inject_contradictions(
        &self,
        target_system: &mut TextProcessingSystem,
        target_beliefs: &[BeliefNode],
        contradiction_strength: f64,
        stealth_level: StealthLevel,
    ) -> AttackResult {
        let mut injection_results = Vec::new();
        
        for belief in target_beliefs {
            // Create contradictory evidence
            let contradictory_evidence = self.generate_contradictory_evidence(belief, contradiction_strength);
            
            // Adjust stealth characteristics
            let mut evidence = contradictory_evidence;
            stealth_level.adjust_evidence_presentation(&mut evidence);
            
            // Inject into belief network
            let injection_result = target_system.inject_evidence(evidence);
            injection_results.push(injection_result);
            
            // Monitor system response
            let system_response = target_system.monitor_response_to_contradiction();
            injection_results.push(system_response);
        }
        
        AttackResult {
            attack_type: AttackType::ContradictionInjection,
            succeeded: injection_results.iter().any(|r| r.caused_belief_change()),
            impact_score: injection_results.iter().map(|r| r.impact_magnitude()).sum(),
            side_effects: injection_results.iter().flat_map(|r| r.side_effects()).collect(),
            detection_risk: stealth_level.compute_detection_probability(self, target_system),
        }
    }
    
    fn generate_contradictory_evidence(&self, belief: &BeliefNode, strength: f64) -> Evidence {
        // Create evidence that directly contradicts the belief
        let contradictory_claim = belief.core_claim.negate();
        
        Evidence {
            claim: contradictory_claim,
            credibility: strength,
            source: self.generate_plausible_source(),
            temporal_validity: Duration::from_secs(3600), // Valid for 1 hour
            supporting_data: self.generate_supporting_data(&contradictory_claim),
        }
    }
}

Temporal Manipulation Attack

impl AttackStrategy {
    fn manipulate_temporal_evidence(
        &self,
        target_system: &mut TextProcessingSystem,
        time_shift: Duration,
        decay_acceleration: f64,
        target_evidence: EvidenceId,
    ) -> AttackResult {
        // Attempt to manipulate temporal aspects of evidence
        let original_evidence = target_system.get_evidence(target_evidence);
        if original_evidence.is_none() {
            return AttackResult::failed("Target evidence not found");
        }
        
        let evidence = original_evidence.unwrap();
        
        // Try to accelerate decay of inconvenient evidence
        let decay_manipulation = target_system.attempt_decay_acceleration(
            target_evidence,
            decay_acceleration
        );
        
        // Try to shift temporal context
        let time_manipulation = target_system.attempt_temporal_shift(
            target_evidence,
            time_shift
        );
        
        // Try to introduce temporal inconsistencies
        let inconsistency_injection = self.inject_temporal_inconsistencies(
            target_system,
            &evidence,
            time_shift
        );
        
        AttackResult {
            attack_type: AttackType::TemporalManipulation,
            succeeded: decay_manipulation.succeeded || time_manipulation.succeeded || inconsistency_injection.succeeded,
            impact_score: decay_manipulation.impact + time_manipulation.impact + inconsistency_injection.impact,
            side_effects: vec![decay_manipulation.side_effects, time_manipulation.side_effects, inconsistency_injection.side_effects].concat(),
            detection_risk: 0.2, // Temporal attacks are often subtle
        }
    }
}

Semantic Spoofing Attack

impl AttackStrategy {
    fn spoof_semantic_meaning(
        &self,
        target_system: &mut TextProcessingSystem,
        original_meaning: &SemanticVector,
        spoofed_meaning: &SemanticVector,
        similarity_threshold: f64,
    ) -> AttackResult {
        // Create text that appears to have original meaning but actually has spoofed meaning
        let spoofed_text = self.generate_semantically_ambiguous_text(
            original_meaning,
            spoofed_meaning,
            similarity_threshold
        );
        
        // Test if system interprets it as intended (original) or as spoofed
        let interpretation_result = target_system.interpret_semantic_meaning(&spoofed_text);
        
        let semantic_distance_to_original = interpretation_result.semantic_vector
            .cosine_similarity(original_meaning);
        let semantic_distance_to_spoofed = interpretation_result.semantic_vector
            .cosine_similarity(spoofed_meaning);
        
        let attack_succeeded = semantic_distance_to_spoofed > semantic_distance_to_original;
        
        AttackResult {
            attack_type: AttackType::SemanticSpoofing,
            succeeded: attack_succeeded,
            impact_score: if attack_succeeded { 
                semantic_distance_to_spoofed - semantic_distance_to_original 
            } else { 0.0 },
            side_effects: vec![
                SideEffect::SemanticConfusion {
                    confusion_level: (1.0 - semantic_distance_to_original).abs()
                }
            ],
            detection_risk: similarity_threshold, // Higher similarity = harder to detect
        }
    }
    
    fn generate_semantically_ambiguous_text(
        &self,
        target_meaning: &SemanticVector,
        spoofed_meaning: &SemanticVector,
        similarity_threshold: f64,
    ) -> String {
        // Use adversarial text generation to create ambiguous content
        let mut text_generator = AdversarialTextGenerator::new();
        
        text_generator.generate_ambiguous_text(
            target_meaning,
            spoofed_meaning,
            similarity_threshold,
            100, // max iterations
        )
    }
}

Integration Testing Framework

pub struct DiggidenTestingSuite {
    attack_batteries: Vec<AttackBattery>,
    fuzzing_engines: Vec<FuzzingEngine>,
    property_validators: Vec<PropertyValidator>,
    continuous_monitoring: ContinuousMonitor,
}

impl DiggidenTestingSuite {
    pub fn run_comprehensive_security_audit(&mut self, target_system: &mut TextProcessingSystem) -> SecurityAuditReport {
        let mut audit_results = Vec::new();
        
        // Run attack batteries
        for battery in &self.attack_batteries {
            let battery_result = battery.execute_attacks(target_system);
            audit_results.push(battery_result);
        }
        
        // Run fuzzing tests
        for fuzzer in &self.fuzzing_engines {
            let fuzzing_result = fuzzer.fuzz_system(target_system, 1000); // 1000 test cases
            audit_results.push(fuzzing_result.into());
        }
        
        // Validate system properties under attack
        for validator in &self.property_validators {
            let property_result = validator.validate_under_attack(target_system);
            audit_results.push(property_result.into());
        }
        
        // Generate comprehensive report
        SecurityAuditReport {
            overall_security_score: self.compute_overall_security_score(&audit_results),
            vulnerability_matrix: self.compile_vulnerability_matrix(&audit_results),
            attack_success_rates: self.compute_attack_success_rates(&audit_results),
            recommended_mitigations: self.generate_mitigation_recommendations(&audit_results),
            critical_vulnerabilities: self.identify_critical_vulnerabilities(&audit_results),
        }
    }
    
    pub fn enable_continuous_monitoring(&mut self, target_system: &mut TextProcessingSystem) {
        self.continuous_monitoring.start_monitoring(target_system);
        
        // Set up real-time attack injection
        self.continuous_monitoring.set_attack_schedule(vec![
            ScheduledAttack::periodic(AttackStrategy::ContradictionInjection { /* ... */ }, Duration::from_secs(300)),
            ScheduledAttack::random(AttackStrategy::SemanticSpoofing { /* ... */ }, 0.1), // 10% chance per operation
            ScheduledAttack::triggered(AttackStrategy::TemporalManipulation { /* ... */ }, TriggerCondition::HighConfidence),
        ]);
    }
}

Key Innovations

1. Continuous Adversarial Testing

Unlike traditional testing that happens once, Diggiden continuously attacks the system during operation.

2. Adaptive Attack Evolution

Attacks evolve based on success/failure patterns, becoming more sophisticated over time.

3. Multi-Vector Attack Strategies

Combines temporal, semantic, belief-based, and contextual attack vectors simultaneously.

4. Stealth Operation Modes

Attacks can be made invisible or disguised as beneficial operations.

5. Integration with Learning

Attack results feed back into Mzekezeke’s belief network for system improvement.

Usage Examples

Basic Vulnerability Assessment

let mut diggiden = DiggidenAdversarialSystem::new();
let mut target_system = TextProcessingSystem::new();

// Run vulnerability scan
let vulnerability_matrix = diggiden.assess_vulnerabilities(&mut target_system);
println!("Most vulnerable component: {:?}", vulnerability_matrix.get_most_vulnerable());

Continuous Security Monitoring

let mut security_suite = DiggidenTestingSuite::new();
security_suite.enable_continuous_monitoring(&mut target_system);

// System now continuously under adversarial testing
// Attacks happen in background during normal operation

This adversarial system ensures that the text processing system remains robust under attack, solving the critical problem of AI systems that work well in ideal conditions but fail when faced with adversarial inputs.