TL;DR
AI coding assistants can hallucinate package names, creating phantom dependencies that don't exist in official repositories. Attackers exploit this predictable behavior through slopsquatting, which involves registering malicious packages with names that AI models commonly suggest. This emerging supply chain attack requires new detection approaches focused on behavioral analysis to complement existing security tools.
Introduction
When AI generates code, it sometimes suggests package names that seem perfect for your needs. These packages might not exist yet, or worse, they exist because an attacker predicted your AI would suggest them. This phenomenon represents a new supply chain risk that requires immediate attention as AI increasingly generates production code.
What Is a Slopsquatting Attack?
Slopsquatting, coined by Python Software Foundation Developer-in-Residence Seth Larson, describes attacks targeting AI code hallucinations. The term combines "slop" (erroneous AI output) with "squatting" (claiming names).
Unlike typosquatting that exploits human typing errors, slopsquatting exploits predictable patterns in how AI models generate package suggestions. Attackers analyze these patterns to identify names that AI frequently suggests but don't exist in legitimate repositories.
The attack unfolds through careful observation and strategic positioning. First, attackers study AI outputs across multiple models and use cases to identify frequently hallucinated package names. According to security researchers, certain hallucinated names appear repeatedly across different AI generation sessions, creating exploitable patterns. Next, attackers register these phantom names before developers encounter them, often including functional code that matches expected purposes while hiding malicious payloads. Finally, when developers use AI-generated code without thorough verification, they unknowingly install these attacker-controlled packages that execute with full dependency permissions.
What Are Phantom Dependencies?
Phantom dependencies are references to non-existent software packages that AI coding assistants generate. These hallucinated package names appear plausible because they follow naming conventions and seem appropriate for requested functionality.
This occurs because AI models predict statistically likely patterns from training data without real-time registry verification. When solving specific problems, an AI might suggest packages like secure-json-validator or enterprise-auth-utils because these names match learned patterns, not because the packages actually exist in any repository.
Common hallucination patterns include context-gap filling where AI creates relevant-sounding names to satisfy user intent, surface-form mimicry where models follow repository naming conventions without validation, and cross-ecosystem borrowing where patterns from one language ecosystem get applied incorrectly to another.
How AI Creates These Vulnerabilities
AI models generate package suggestions through pattern matching rather than real-time registry queries. When configured with higher creativity settings, models become more likely to invent plausible-sounding package names. The models combine common package name components that frequently appear together in their training data, creating suggestions that seem legitimate but may not exist.
Developer behavior amplifies this risk through what some call "vibe coding," where suggestions get rapidly accepted with minimal verification. Since the generated code appears syntactically correct and the package names seem reasonable, phantom dependencies can enter codebases without proper review. If attackers have registered these package names by build time, malicious code enters the software supply chain.
Detection and Protection Strategies
Runtime Detection Challenges
Security tools designed for known vulnerabilities need new approaches to detect slopsquatted packages effectively. Static analysis tools check against vulnerability databases that don't include newly created packages. Dynamic testing often lacks the visibility needed to observe package resolution behaviors. Traditional composition analysis focuses on known vulnerabilities rather than behavioral anomalies.
Effective detection requires behavioral analysis that goes beyond signature matching. This includes tracking package resolution attempts and identifying unusual patterns, monitoring how packages behave relative to their stated purpose, detecting when packages make system calls or network connections outside their expected scope, and identifying packages that access sensitive resources beyond their documented functionality.
Development Safeguards
Code Review Excellence: Implement mandatory human review specifically for AI-generated dependencies. Verification should confirm that packages exist in official repositories, have legitimate maintainers with established history, and show consistent download patterns over time.
AI Configuration Best Practices: Configure coding assistants with appropriate creativity settings that balance innovation with safety. Restrict the context window to exclude outdated code examples that might suggest deprecated packages.
Automated Verification Systems: Deploy pre-commit hooks that validate new dependencies against approved package lists. Flag any packages published within suspicious timeframes or those lacking established download history. Consider implementing private registries or proxies that cache and scan packages before they enter your development pipeline.
Supply Chain Security Evolution
Modern supply chain security requires comprehensive Software Bill of Materials (SBOM) tracking to maintain visibility into all dependencies. Organizations should monitor public registries for names matching their internal conventions or common AI hallucination patterns.
Deploy monitoring solutions that establish baseline behaviors for packages and alert on anomalies. Most importantly, develop incident response procedures specifically designed for supply chain compromises, as traditional response playbooks may not address the unique challenges of dependency-based attacks.
Key Indicators of Slopsquatting
Security teams should watch for specific behavioral patterns that indicate potential slopsquatting. These include parsing libraries that unexpectedly make network connections, utility packages accessing system files beyond their stated scope, authentication modules executing system commands, packages with recent registration dates and unknown maintainers, or simple functionality packages containing obfuscated code sections.
Frequently Asked Questions
How do slopsquatted packages evade traditional security scanning? Traditional scanners primarily check against databases of known vulnerabilities. Slopsquatted packages are newly created with functional code that passes standard checks, making behavioral analysis essential for detection.
Which development practices increase slopsquatting risk? Rapid prototyping with AI assistance, minimal code review for dependencies, high AI temperature settings, and automated dependency updates without verification all increase exposure to this attack vector.
What makes AI-generated package names predictable? AI models trained on similar codebases tend to generate consistent naming patterns. Different models show preferences: some favor verbose descriptive names, others suggest technically precise terms, and some recommend variations of existing packages.
Conclusion
The intersection of AI-assisted development and software supply chains creates attack vectors that traditional security approaches need help addressing. Slopsquatting represents an evolution from reactive vulnerability exploitation to proactive positioning based on predictable AI behaviors.
Organizations must implement multi-layered defenses combining developer awareness, automated verification, behavioral monitoring, and adapted incident response procedures. The key lies not in avoiding AI development tools but in understanding their limitations and implementing appropriate controls throughout the development lifecycle.
As AI-generated code becomes more prevalent, maintaining visibility into both package resolution and runtime behavior becomes essential. By combining preventive measures in development with behavioral detection in production, organizations can safely harness AI's productivity benefits while protecting against this emerging threat.
