Analyzing Security Risks Introduced by AI-Generated Code Dependencies

Introduction

The software development landscape is undergoing a rapid transformation fueled by the increasing adoption of AI coding assistants like GitHub Copilot, Google Gemini, and Amazon Q Developer [1]. Industry surveys highlight a dramatic rise in their usage, with a significant majority of developers now leveraging these tools on a daily or weekly basis [1]. These powerful assistants offer compelling benefits, including accelerating development cycles, automating boilerplate code generation, and reducing repetitive tasks [2]. Developers using generative AI tools frequently report substantial speed increases, sometimes completing tasks up to twice as fast [2].

However, this powerful assistance introduces potential downsides. While AI-generated code can be incredibly helpful, it can also introduce significant new security risks [0]. One of the most critical areas of concern involves the external dependencies that AI assistants might suggest or include in the code they generate [3]. The AI might propose outdated libraries with known vulnerabilities, or even invent package names entirely, leading to potential supply chain attacks if developers are not sufficiently vigilant [3].

This post will explore how AI coding assistants can introduce security vulnerabilities specifically through dependency-related issues. We will examine the various types of risks involved, analyze their potential impact, and outline practical strategies for identifying and mitigating these threats within your development workflow [4]. Successfully navigating this new era of AI-assisted development requires a deep understanding of both the power and the potential pitfalls these tools present, particularly concerning the integrity of the software supply chain [4].

The AI Coding Assistant Landscape and Dependency Generation

AI coding assistants, encompassing tools like GitHub Copilot, Tabnine, and Amazon Q Developer, operate within a diverse and rapidly evolving market [5]. These tools are fundamentally built upon large language models (LLMs) [6]. These models are trained on massive datasets that typically include vast amounts of publicly available source code scraped from platforms like GitHub, Stack Overflow, and various technical documentation [6]. Through this extensive training, the models learn the syntax, structure, common patterns, and semantics of numerous programming languages [6].

When a developer provides a prompt, whether in natural language or as partial code, the AI assistant processes this input and generates relevant code suggestions [6]. This generated code frequently incorporates import statements and references to external libraries or packages, commonly known as dependencies [7]. This behavior stems from the AI learning patterns prevalent in its training data, where utilizing dependencies to leverage existing functionality is a common and fundamental practice in modern software development [8]. AI assistants often analyze the existing codebase context to suggest dependencies that appear to fit the project's needs, aiming to automate common tasks and reduce the need for writing boilerplate code from scratch [7], [8].

However, it's crucial to understand that AI output is inherently non-deterministic or probabilistic [9]. Even when given the exact same prompt, an AI model might produce slightly different code each time, including variations in the suggested dependencies [9]. This inherent variability, coupled with the fact that the AI's knowledge is based on training data that may be outdated or contain flawed examples, has significant implications for the accuracy and security of the dependencies it references [9]. Consequently, developers cannot blindly trust the dependency suggestions provided by AI tools [9].

Specific Security Risks Introduced by AI-Generated Dependencies

The convenience offered by AI-generated code, particularly its handling of dependencies, comes with several specific security risks that development teams must actively understand and address [10]. These risks can range from frustrating build failures to severe software supply chain compromises.

• Hallucinated Dependencies:

  • AI models are prone to "hallucinate," meaning they can invent plausible-sounding but entirely non-existent library, package, or module names within the code they generate [11], [12]. Research has confirmed this is a frequent occurrence; one study found that nearly 20% of package suggestions across various LLMs were non-existent [11]. While commercial models showed lower hallucination rates (around 5%), open-source models exhibited significantly higher rates (over 20%) [11], [12].
  • Risk: This phenomenon creates a significant supply chain risk known as "slopsquatting" [11]. Attackers can monitor for these commonly hallucinated names and proactively publish malicious packages under those specific names in public repositories like PyPI or npm [13]. If a developer using AI-generated code attempts to install the hallucinated dependency, they might inadvertently download and execute the attacker's malicious code [13]. This method exploits the AI's behavior directly, rather than relying on a simple human typing error [13].
  • Risk: Even without malicious exploitation, hallucinated dependencies lead to broken builds when the package manager fails to locate the non-existent package [14]. This results in wasted development time spent debugging the issue [14].

• Incorrect or Insecure Dependency Versions:

  • AI models, trained on historical data, may suggest outdated dependency versions that contain known vulnerabilities (CVEs) [15], [16]. The AI lacks real-time access to dynamic vulnerability databases and may not be aware of security flaws discovered after its training data was compiled [16].
  • The AI might suggest unstable or beta versions of libraries [17]. These versions are typically unsuitable for production environments and could contain unknown bugs or security flaws, a consequence of the diverse, sometimes experimental, code included in training data [17].
  • The AI could specify overly broad version ranges in package manifests [18]. This practice, sometimes referred to as "version promiscuity," might inadvertently pull in future releases of a dependency that contain newly introduced vulnerabilities, potentially leading to security breaches [18].
  • AI tools, often lacking deep contextual understanding of a specific project's existing dependency tree, might suggest dependencies that are incompatible or conflict with libraries already in use [19]. This can result in frustrating "dependency hell," runtime errors, and unpredictable application behavior [19].

• Insecure Usage Patterns of Legitimate Dependencies:

  • Even if a dependency itself is legitimate and secure, the AI might generate code that uses its functions or features in an insecure way [20].
  • This can include generating code that fails to perform proper input validation or sanitization when interacting with a data processing library, leaving the application vulnerable to injection attacks like SQL injection or Cross-Site Scripting (XSS) [21]. AI might also generate code that assumes insecure default configurations for frameworks or libraries [21].
  • AI-generated code may omit necessary security checks, error handling, or boundary checks around calls to dependency functions [22]. This can create security blind spots exploitable by attackers or lead to application crashes [22], [37]. Missing proper error handling might also inadvertently expose sensitive system details if errors occur [26].
  • The AI might suggest using deprecated or less secure APIs within a library [23]. This could happen because those patterns were common in its older training data [23]. Using deprecated functions increases the risk of encountering known, unpatched vulnerabilities [23].

• Implicit or Hidden Dependencies:

  • AI might generate code that implicitly relies on a specific dependency, environment configuration, or even another library (a transitive dependency) that isn't explicitly included in the suggested code snippet or import statements [24], [25]. These "hidden" dependencies, often brought in transitively, can contain vulnerabilities unknown to the developer [24].
  • Risk: This can lead to difficult-to-diagnose runtime errors, unexpected application behavior, or security blind spots if the implicit requirement isn't met or if the hidden dependency itself is vulnerable [25], [26]. Errors resulting from these hidden issues might even expose sensitive system details [26].

• Dependencies Sourced from Potentially Untrusted Data:

  • The vast datasets used to train AI models are often uncurated mixes of code from the internet [27], [28]. Some of this code may contain malicious elements, insecure patterns, or references to insecure dependencies [27], [28].
  • Risk: The AI model might learn from and replicate these insecure patterns, inadvertently "recommending" or including references to known bad packages, vulnerable library versions, or insecure repositories in its generated code [ref:ref-28, ref-29]. Malicious actors could also intentionally "poison" training data to introduce vulnerabilities or backdoors that the AI then propagates [27], [29].

The Potential Impact of Compromised or Risky Dependencies

Introducing compromised or risky dependencies into a software project, whether through manual coding errors or AI-generated suggestions, can have severe consequences [30]. Understanding these potential impacts underscores the critical importance of diligent dependency management practices.

• Supply Chain Attacks:

  • One of the most significant risks is the facilitation of software supply chain attacks [31]. Attackers can inject malicious code into seemingly legitimate dependencies or create fake packages that mimic real ones (typosquatting) or exploit AI hallucinations ("slopsquatting") [31], [32]. When these compromised dependencies are included in a project, the malicious code executes within the application's trusted context [32].
  • This can compromise the application build process, injecting malware or backdoors during compilation, or affect the application's runtime behavior, potentially leading to data theft or system takeover [33].

• Introduction of Known Vulnerabilities (CVEs):

  • Using dependencies with known vulnerabilities (CVEs), often suggested by AI tools trained on older data, directly introduces exploitable flaws into the application [34].
  • These flaws, stemming from outdated or vulnerable library versions, can be exploited by attackers to cause data breaches, denial of service (DoS) conditions, or privilege escalation, allowing unauthorized access to sensitive data or system functions [35], [36].

• Runtime Errors and Stability Issues:

  • Incorrect dependencies, incompatible versions, or flawed code within dependencies can lead to frequent runtime errors, application crashes, and unexpected behavior [37], [38]. This significantly reduces the application's reliability and degrades the user experience [38]. Issues like missing boundary checks or null reference errors in AI-generated dependency usage can directly cause crashes [37].

• Increased Technical Debt and Maintenance Overhead:

  • Dealing with problematic dependencies inevitably increases technical debt [39]. AI-generated code, sometimes prioritizing speed over quality, can exacerbate this by introducing complex, poorly documented, or duplicated code related to dependencies [39].
  • Significant development time is often consumed debugging elusive issues caused by bad dependencies or version conflicts [40]. Surveys suggest developers spend considerable extra time debugging AI-generated code [40].
  • Identifying, vetting, and replacing risky dependencies requires substantial effort, involving security scans, code changes, and thorough testing, adding significantly to the overall maintenance overhead [41].

• Violation of Compliance and Security Standards:

  • Many industries are subject to regulations (e.g., GDPR, HIPAA, PCI DSS) and security standards (e.g., NIST SSDF, EU Cyber Resilience Act) that mandate secure software development practices, including secure dependency management [42], [43].
  • Failure to properly vet and manage dependencies, including those introduced by AI, can lead to non-compliance, resulting in legal penalties, significant fines, and severe reputational damage [42]. Issues like license violations from AI-generated code can also cause compliance problems [42].

Identifying and Detecting Risky AI-Generated Code and Dependencies

Given the potential risks, proactively identifying and detecting problematic AI-generated code and dependencies is absolutely crucial [44]. A multi-layered approach combining rigorous manual scrutiny with robust automated tooling offers the most effective defense.

• Enhanced Manual Code Review:

  • Developers must fundamentally shift their mindset: treat AI suggestions as potential starting points or first drafts, never as final, production-ready solutions [46]. Blindly trusting AI output creates significant risk [62].
  • Code reviews need a specific focus on AI-generated code. Reviewers should examine import statements and package manifests (requirements.txt, package.json, pom.xml, etc.) with extra care [47]. Understanding how dependencies are used within the generated code is just as important as knowing which dependencies are used [47]. Reviewers need training to spot AI-specific anti-patterns like insecure defaults or missing input validation [45].
  • Crucially, reviewers must verify suggested dependency names and versions against known, trusted sources like official package repositories [48]. Due to the risks of hallucinations and "slopsquatting," never assume an AI-suggested package name is real or safe without independent verification [48], [47].

• Leveraging Automated Security Tools:

  • Automated tools are essential for keeping pace with the speed of AI-assisted development and identifying issues humans might miss [49].
  • Software Composition Analysis (SCA): Tools like npm audit, pip-audit, OWASP Dependency-Check, Snyk, and others automatically scan the dependencies listed in project manifests against databases of known vulnerabilities (CVEs) [50]. They are vital for uncovering risks in both direct and transitive dependencies potentially introduced by AI [50].
  • Static Application Security Testing (SAST): SAST tools (e.g., SonarQube, Checkmarx, Snyk Code) analyze the source code itself without executing it to find insecure coding patterns [51]. This includes detecting how dependencies are used, such as improper input handling when interacting with a library or other unsafe usage patterns within the AI-generated code [51]. Many modern SAST tools also incorporate AI to improve their detection capabilities [49].
  • Dynamic Application Security Testing (DAST): DAST tools (e.g., OWASP ZAP) test the running application from the outside, simulating attacks to find vulnerabilities that only manifest at runtime [52]. These can potentially arise from complex interactions between AI-generated code and its dependencies [52].
  • Dependency Firewall/Proxy: Tools like Sonatype Repository Firewall or Bytesafe Dependency Firewall act as gatekeepers for dependency downloads [53]. They can block or flag attempts to download packages that are unknown, known to be malicious, contain high-severity vulnerabilities, or violate organizational policies, providing a crucial defense against risky dependencies entering the environment [53].

• Implementing Strict Dependency Verification Processes:

  • Establish formal processes to verify dependencies before they are integrated into a project [54]. This includes maintaining an inventory (SBOM) and using techniques like version pinning and hash verification to ensure integrity [54].
  • Require explicit approval for adding any new dependency to a project [55]. This mandates a security assessment and justification, preventing the casual introduction of risky components, whether suggested by AI or a developer [55].
  • Use internal, curated dependency registries or repositories [56]. By pulling approved dependencies from a trusted internal source rather than directly from potentially compromised public repositories, organizations can prevent the accidental download of malicious packages, including those resulting from AI hallucinations or typosquatting [56].
  • Automate vulnerability checks against databases like NVD or OSV before a dependency is formally added or approved [57]. This "shift-left" approach prevents known vulnerable components from entering the codebase in the first place [57].

• Integrating Security Checks into the CI/CD Pipeline:

  • Embed automated security testing directly into the Continuous Integration/Continuous Deployment (CI/CD) pipeline [58]. This ensures security is checked continuously as code is developed and evolves.
  • Run SCA, SAST, and potentially DAST scans automatically on every code commit or build [59]. This provides immediate feedback to developers about vulnerabilities introduced by their changes, including those involving AI-generated code or dependencies [59].
  • Configure the CI/CD pipeline to automatically fail the build if high-risk dependency issues (e.g., critical or high-severity CVEs) are detected by SCA tools [60]. This acts as a crucial security gate, preventing vulnerable code from being merged or deployed [60].

Mitigation Strategies and Best Practices

Successfully leveraging AI coding assistants while managing their inherent risks requires a proactive and layered security strategy [61]. Combining human diligence with automated safeguards and clear organizational policies is key to building secure software in the age of AI.

• Treat AI Output as a Suggestion: This is the foundational principle for secure AI-assisted development. Never blindly accept AI-generated code, especially import statements or dependency configurations [62]. Always assume it requires rigorous review and validation, treating it as a potentially flawed first draft [62].
• Establish Clear Dependency Management Policies:
  • Define and enforce policies specifying approved sources for dependencies, restricting developers from pulling packages from untrusted or unvetted locations [63], [64]. Consider using internal curated registries as a primary source [56].
  • Specify strict versioning policies [65]. Avoid wide version ranges that can inadvertently pull in unexpected or vulnerable updates. Prefer pinning exact versions or allowing only minor/patch updates automatically, requiring manual review for major version changes [65].
  • Mandate regular auditing and updating of all dependencies [66]. Use automated tools to scan for vulnerabilities and prompt updates, and actively remove unused dependencies to reduce the attack surface [66].
• Strengthen Code Review Processes:
  • Combine automated scanning with rigorous manual peer reviews [67]. Human reviewers are essential for understanding context, logic, and spotting subtle flaws AI might introduce that tools miss [67].
  • Train developers and security teams specifically on how to identify common AI-related dependency pitfalls, such as hallucinated package names, suggestions for outdated versions, or insecure usage patterns [68].
  • Implement peer reviews with an explicit security focus, critically examining authentication, authorization, input validation, and dependency usage within the generated code [69]. Ensure reviewers truly understand the code they are approving, especially if it's AI-generated [67].
• Utilize Secure Development Frameworks and Libraries:
  • Leverage established secure development frameworks (e.g., OWASP guidelines, NIST SSDF) and libraries with built-in security features [70]. These provide a more secure foundation than relying solely on AI suggestions for common tasks [70].
  • Prefer well-maintained, widely-used libraries with strong security track records and active communities [71]. These are more likely to be scrutinized for vulnerabilities and patched quickly [71].
  • Adhere to language-specific secure coding guidelines (e.g., CERT Java, OWASP Go-SCP) when reviewing and refining AI-generated code [72]. Ensure the AI's output conforms to these established standards [72].
• Maintain an Inventory of Approved Dependencies:
  • Keep a formal inventory or Software Bill of Materials (SBOM) of all dependencies used in your projects [73]. This provides crucial visibility for vulnerability management and compliance [73].
  • Maintain an explicit list or internal repository of vetted libraries and their approved versions [74]. This ensures only components that have passed organizational security scrutiny are used [74].
  • Use tools integrated into the CI/CD pipeline (like dependency firewalls or SCA tools with policy enforcement) to ensure projects only use dependencies from this approved list, failing builds or deployments otherwise [75].
• Developer Education and Training:
  • Ongoing education is critical for the entire development team [76]. Educate developers thoroughly on the specific security risks associated with AI-generated code and dependencies, including vulnerabilities, hallucinations, and data privacy concerns [77].
  • Provide practical training on secure coding practices, secure dependency management techniques, and how to effectively review and validate AI output [78]. Emphasize critical thinking and the developer's ultimate responsibility for the code they commit [77].
• Stay Updated on AI Security Best Practices:
  • The field of AI security is rapidly evolving [79]. Development and security teams must stay informed about emerging threats, new research, and evolving best practices specifically addressing AI code generation security [79].
  • Follow research and guidelines from organizations like OWASP, NIST, and others that are actively working on defining secure practices for AI development and usage [80].

Conclusion

AI coding assistants undoubtedly offer significant benefits, boosting developer productivity and accelerating software delivery timelines [82]. They automate tedious tasks, generate boilerplate, and provide instant code suggestions, fundamentally changing the way we build software [82]. However, this power comes with critical security considerations that cannot be ignored [81]. The potential for AI to generate insecure code, suggest outdated or vulnerable dependencies, or even hallucinate non-existent packages poses substantial risks to the integrity of the software supply chain [82].

Addressing these challenges requires increased vigilance and a proactive security posture from development teams and organizations alike [83]. Relying solely on the speed and convenience of AI without implementing adequate safeguards can lead to the introduction of vulnerabilities, increased technical debt, and compliance issues [83]. A false sense of security is perhaps one of the greatest dangers in this new paradigm [83].

The key to navigating this new landscape lies in a multi-layered strategy that combines human expertise with automated tools [84]. This involves treating AI output as a suggestion requiring rigorous manual review, leveraging automated security tools (SAST, SCA, DAST) integrated throughout the CI/CD pipeline, establishing and enforcing strict dependency management policies, and prioritizing continuous developer education on secure coding and AI-specific risks [84].

The future of software development is undeniably AI-assisted [85]. As these tools become more sophisticated and deeply integrated into our workflows, the imperative to embed security early and continuously throughout the development lifecycle becomes even stronger [85]. By embracing DevSecOps principles and adapting our security practices to the unique challenges posed by AI, we can effectively harness the innovative potential of AI-driven development while building more secure and resilient software [85].