HalluSquatting: Weaponizing AI Hallucinations — Building Agent Botnets by Exploiting LLM Fabrication Vulnerabilities

"""
HalluSquatting Attack Framework: Core Concepts
"""

class HalluSquattingAttack:
    """
    Complete adversarial hallucination squatting framework.
    
    Attack flow:
    1. Analyze LLM hallucination distribution for specific resource names
    2. Predict which non-existent resources the LLM most likely "fabricates"
    3. Pre-register those resources with malicious payloads
    4. Wait for agents to trigger hallucinations during innocent tasks
    """
    
    def __init__(self, target_model: str):
        self.target_model = target_model
        self.registered_squats = {}
        self.trigger_stats = {"attempts": 0, "successes": 0}
    
    def analyze_hallucination_patterns(self, trending_resources: list, 
                                       sample_size: int = 1000) -> dict:
        """Analyze which non-existent resources the model hallucinates most"""
        hallucination_map = {}
        
        for resource in trending_resources:
            fake_results = self._query_model_for_resource(resource, sample_size)
            for result in fake_results:
                hallucination_map[result] = hallucination_map.get(result, 0) + 1
        
        return {
            resource: count / sample_size
            for resource, count in sorted(
                hallucination_map.items(), key=lambda x: x[1], reverse=True
            )[:20]
        }
    
    def _query_model_for_resource(self, resource: str, n_queries: int) -> list:
        """Simulate querying the model for resource locations"""
        import random
        results = []
        for _ in range(n_queries):
            if random.random() < 0.15:
                results.append(f"github.com/real/{resource}")
            else:
                fake_name = self._generate_fake_name(resource)
                results.append(f"github.com/fake/{fake_name}")
        return results
    
    def _generate_fake_name(self, resource: str) -> str:
        """Generate hallucinated names based on real resource names"""
        import random
        patterns = [
            f"{resource}-v{random.randint(2, 9)}",
            f"{resource}-{random.choice(['beta', 'stable', 'lite', 'pro'])}",
            f"{resource}-{random.choice(['toolkit', 'sdk', 'cli', 'lib'])}",
        ]
        return random.choice(patterns)
    
    def register_squat(self, hallucinated_name: str, 
                       malicious_payload: dict) -> bool:
        """Register the hallucinated resource with malicious payload"""
        self.registered_squats[hallucinated_name] = {
            "payload": malicious_payload,
            "times_triggered": 0,
        }
        return True
    
    def execute_attack_chain(self, agent_task: str) -> dict:
        """Execute the full attack chain"""
        self.trigger_stats["attempts"] += 1
        import random
        
        # Step 1: Agent hallucinates during task processing
        hallucinated = random.choice(
            list(self.registered_squats.keys()) + [None]
        ) if random.random() < 0.85 else None
        
        if not hallucinated or hallucinated not in self.registered_squats:
            return {"success": False}
        
        # Step 2: Agent downloads and executes malicious content
        squat = self.registered_squats[hallucinated]
        self.trigger_stats["successes"] += 1
        squat["times_triggered"] += 1
        
        return {
            "success": True,
            "hallucinated_name": hallucinated,
            "payload_type": squat["payload"].get("type"),
        }


# Comparison with Typosquatting
class AttackComparison:
    """Compare HalluSquatting with traditional Typosquatting"""
    
    @staticmethod
    def compare():
        comparison = {
            "Dimension": [
                "Target", "Trigger Mechanism", "Attack Vector",
                "Scale", "Detection Difficulty", "Defense"
            ],
            "Typosquatting": [
                "Human typing errors", "User manually types wrong URL",
                "Requires social engineering", "One user per attack",
                "Medium (check URL spelling)", "URL blacklists, whitelists"
            ],
            "HalluSquatting": [
                "LLM fabrication behavior", "Agent autonomously hallucinates",
                "No human involvement needed", "One registration = millions of agents",
                "Extremely high (hallucinations look plausible)",
                "Model must refuse to guess"
            ],
        }
        
        print("=" * 80)
        print("HalluSquatting vs Typosquatting: Key Differences")
        print("=" * 80)
        for i in range(len(comparison["Dimension"])):
            print(f"\n{comparison['Dimension'][i]}:")
            print(f"  Typosquatting:    {comparison['Typosquatting'][i]}")
            print(f"  HalluSquatting:   {comparison['HalluSquatting'][i]}")
        print("=" * 80)


if __name__ == "__main__":
    AttackComparison.compare()
    
    # Simulate attack
    attack = HalluSquattingAttack("Claude Fable 5")
    attack.register_squat("react-router-v3", {"type": "code_execution", "payload": "malicious"})
    attack.register_squat("express-validator-beta", {"type": "botnet_join", "c2": "c2.botnet"})
    
    print("\nAttack Chain Results:")
    for task in ["Install React Router", "Add Express validation", "Update lodash"]:
        result = attack.execute_attack_chain(task)
        icon = "✅" if result["success"] else "❌"
        print(f"  {icon} {task}: {result}")

// Go: Hallucination transferability analysis
package main

import (
    "fmt"
    "strings"
)

type HallucinationSample struct {
    ModelName       string
    ResourceName    string
    IsHallucination bool
}

func main() {
    models := []string{"Cursor", "Copilot", "Gemini CLI", "Claude Code", "OpenClaw"}
    resources := []string{"react-router-v3", "express-validator-beta", "lodash-pro"}
    
    // Simulate hallucination data
    modelHallucinations := make(map[string]map[string]bool)
    for _, model := range models {
        modelHallucinations[model] = make(map[string]bool)
        for _, resource := range resources {
            isHall := strings.HasPrefix(resource, "react") || 
                     strings.HasPrefix(resource, "express")
            modelHallucinations[model][resource] = isHall
        }
    }
    
    // Compute transferability
    fmt.Println(strings.Repeat("=", 70))
    fmt.Println("Hallucination Transferability Matrix")
    fmt.Println(strings.Repeat("=", 70))
    
    for i := 0; i < len(models); i++ {
        for j := i + 1; j < len(models); j++ {
            setA := modelHallucinations[models[i]]
            setB := modelHallucinations[models[j]]
            
            intersection, union := 0, len(setA)
            for r := range setB {
                if setA[r] { intersection++ } else { union++ }
            }
            
            similarity := float64(intersection) / float64(union) * 100
            fmt.Printf("  %s ↔ %s: %.0f%% overlap\n", 
                       models[i], models[j], similarity)
        }
    }
    
    fmt.Println("\n" + strings.Repeat("=", 70))
    fmt.Println("Key Finding: Hallucinations transfer across models at")
    fmt.Println("~72% Jaccard similarity, enabling single-attack, multi-model exploitation.")
}