SSCGuard: Mitigating LLM-Based SSC Threats

• SSCGuard is a security framework that detects, analyzes, and mitigates external component threats in software supply chains arising from LLM-generated code.
• It employs prompt-based defenses like Chain-of-Confirmation and middleware integrations to validate code by querying official registries and repositories.
• Empirical analysis of over 439,000 prompt-response pairs reveals high rates of fabricated and outdated components, underscoring the need for automated SSC mitigation.

SSCGuard is a security analysis and mitigation framework targeting threats within the software supply chain (SSC) that emerge from automatically generated code, particularly code produced by LLMs. As extensive empirical analysis demonstrates, LLMs frequently introduce external component references—packages, libraries, CI plugins, domains, or repositories—that may be fabricated, outdated, or known to be vulnerable, posing significant risks of workflow hijacking, latent exploitation, or long-term compositional vulnerabilities. SSCGuard is designed to systematically identify, analyze, and defend against these threat vectors by simulating realistic developer interactions, integrating registry and repository validation through automated RESTful queries, and applying prompt-based and middleware-based defenses to reduce fabrication and inform users of SSC risks (Li et al., 24 Sep 2025).

1. SSCGuard: Objectives and Workflow

SSCGuard's chief goal is to establish empirically grounded insight into SSC threats posed by LLM-generated code and to operationalize mitigations that can be realistically incorporated into development workflows. The framework ingests large-scale, real-world coding queries—principally sourced from Stack Overflow—transforms them into prompts, and feeds them into representative LLMs (including GPT-series and Llama-series). The LLM responses, encompassing both source code and CI configuration files, are mined for references to external components (packages, libraries, domains, repositories, CI plugins). For each such reference, SSCGuard performs validation by querying authoritative package registries and APIs (e.g., npm, PyPI, GitHub), thereby determining the existence, validity, and security posture of referenced resources.

A summary of the SSCGuard workflow is given below:

Stage	Description	Technique/Interface
Prompt	Transform real developer queries into LLM prompts	Stack Overflow mining
Generation	LLM (GPT, Llama) code/CI configuration generation	LLM querying
Extraction	Identify external components in generated outputs	Text parsing, regex, NLP
Validation	Confirm existence, version, and security status	RESTful API, HTTPS, GitHub API
Detection	Classify threat categories (hallucination, vulnerable, outdated)	Threat taxonomy matching
Defense	Apply prompt- or middleware-based mitigations	CoC scheme, ARGUS integration

This systematic approach allows SSCGuard to reveal the latent and explicit SSC threats embedded within the recommendations of widely-deployed LLMs.

2. Taxonomy of Software Supply Chain Threats

SSCGuard's analysis is built on a detailed threat taxonomy mapping the ways LLMs introduce risk into the SSC through code and CI configuration generation. The paper identifies three overarching categories, each comprising multiple concrete threats:

A. External Component Hallucination

• Package Hallucination: LLMs fabricate package names (or versions) that do not exist in official registries. These may appear in code (e.g., npm install) or CDN links. Attackers can register these names for malicious intent.
• Domain Hallucination: Reference to pseudo-random domains in generated URLs; attackers may register these "dangling" domains to intercept traffic.
• GitHub Account Hallucination: Codes referencing fictitious GitHub accounts or repositories; such accounts are susceptible to adversarial registration.
• CI Plugin Hallucination: CI workflow files may include non-existent (GitHub Actions or others) plugins.
• CI Plugin Version Hallucination: Plugins versions suggested by LLMs may not exist (e.g., "v23" where only "v3" exists).
• CI Plugin Version Reuse: Reference to a plugin version by branch/tag that might later be deleted and repurposed, allowing hostile modification.

B. Vulnerable External Components

• Vulnerable Third-party Versions: LLMs recommend package/library versions with pre-existing vulnerabilities.
• Vulnerable CI Configurations: Generated CI workflows may be structured without proper input sanitization, opening injection vectors.

C. Outdated External Components

• Resource/Service Absence: Resource URLs or libraries referenced but no longer available (HTTP 404).
• Package Deprecation: Usage of deprecated or unpublished packages, left open to future takeover or exploitation.
• CI Plugin Redirection Hijacking: Old plugin addresses redirecting to new ones; the original address can be reregistered and hijacked.

These threats, if undetected, expose integrating developers and end-users to software supply chain compromise, workflow takeovers, or silent backdooring.

3. Risks of LLM-Generated Code in SSC Contexts

SSCGuard's paper reveals that LLM-generated code exhibits three primary risk modalities:

• Fabrication (Hallucination): Generated outputs may reference entities (packages, domains, plugins) that are plausible yet non-existent, due to the LLM's generalization or completion heuristics over partial/ambiguous developer prompts.
• Misleading Information: LLMs may suggest authentic but known-vulnerable external components, due to static model training on historical (now outdated) or compromised codebases.
• Outdated Data: Referenced dependencies may have changed state post-training, causing the LLM to recommend deprecated/unmaintained resources with increased risk of future hijacking or exploits.

Empirical testing across 439,138 prompt–response pairs, processed via four popular LLMs, reveals that such risks persist independently of the model’s family or release date (hallucinated packages ranged 33–50+, hallucinated CI plugins over 70% in some models, with frequency and nature varying by ecosystem). Notably, prompts soliciting "multiple packages/plugins" tend to induce higher fabrication, as LLMs fill perceived informational gaps by inventing additional names.

4. Defensive Mechanisms: Chain-of-Confirmation and Middleware

SSCGuard proposes and evaluates two complementary defense strategies:

A. Prompt-based Defense: Chain-of-Confirmation (CoC)

The Chain-of-Confirmation protocol iteratively queries the LLM, using multi-stage interaction:

1. Extraction: All external component references from an initial LLM response are programmatically extracted.
2. Confirmation: A follow-up prompt instructs the LLM to confirm the existence of these components by "checking" against registries, expecting a binary ("Yes"/"No") confirmation.
3. Regeneration: The LLM is re-prompted to generate code using only validated components.

This reduces hallucination rates significantly; for example, package hallucination is lowered from ~7.5% to ~3.6% without reducing the volume of total valid recommendations. The CoC modality thus effectively interleaves prompt engineering with automated verification to limit LLM-induced SSC threats.

B. Middleware-based Defense: SSCGuard as Intermediary

SSCGuard is further operationalized as a middleware—deployed as a CI-integrated check or browser extension—that intercepts LLM-generated code prior to developer integration. It performs the following:

• Automatic Extraction of all external component mentions.
• RESTful Registry/Repository Validation using APIs (e.g., npm, PyPI, GitHub) via queries such as https://registry.npmjs.org/{package-name}.
• Security and Existence Alerts: Issues warnings if non-existent, vulnerable, or deprecated resources are detected.
• ARGUS Static Analysis: For CI configurations, integrates with taint analysis tools to flag unsanitized or risky workflows.

These alerts serve to disrupt or delay the integration of insecure LLM output into live codebases, prescribing a "defense in depth" approach.

5. Empirical Findings and Implications

The extensive prompt–response empirical dataset produced by SSCGuard surfaces the following quantitative findings:

• Fabrication is Prevalent: Hallucinated package rates between 33%–50+% across models; hallucinated CI plugins exceeding 70% in some cases.
• Vulnerabilities and Outdatedness: Many recommendations incorporate known-vulnerable or deprecated component versions.
• Prompt Structure Affects Risk: More open-ended or multi-component prompts lead to higher hallucination rates due to the LLM’s bias toward "filling" the expected output.
• Defense Effectiveness: The Chain-of-Confirmation defense mechanism approximately halves hallucination rates in select settings, while preserving the yield of usable recommendations.

A plausible implication is that without automated defense and validation, LLM-assisted development pipelines using direct model outputs are consistently vulnerable to SSC-specific attack vectors, many of which are non-obvious and long-lived.

6. Implementation Details and Technical Methodologies

SSCGuard employs a suite of RESTful queries and signal-processing methods to operationalize its analysis:

• Existence Checks: For package verification, SSCGuard issues HTTP GET requests to registry endpoints, e.g., https://registry.npmjs.org/{package-name}, with HTTP 404 indicating a hallucination.
• Domain Verification: HTTPS requests are issued to referenced domains; errors or exceptions signal non-existence.
• Semantic Similarity: To discern overgeneralization or prompt-copying, SSCGuard encodes package names and prompt terms into vector space (e.g., using BERT), then applies cosine similarity:

$$\cos(\vec{a}, \vec{b}) = \frac{\vec{a} \cdot \vec{b}}{||\vec{a}|| \cdot ||\vec{b}||}$$

A threshold (0.6) is applied to flag excessive semantic similarity.

• CI Configuration Analysis: Integration with ARGUS and similar static taint analysis tools enhances detection of workflow vulnerabilities and plugin misuse.

These technical methods support both real-time middleware deployment and large-scale, retrospective analytics.

7. Recommendations for Secure LLM-Assisted Development

SSCGuard’s results highlight critical engineering recommendations:

• Verification Imperative: Never directly trust LLM-generated code containing external component references without validation (middleware-based or via prompt interaction).
• Defense in Depth: Combine prompt engineering defenses (such as Chain-of-Confirmation) with automated middleware scanning.
• CI Configuration Scrutiny: Rigorously review all CI pipeline code and plugin usage, as these are especially susceptible to hallucination and hijacking.
• Continuous Inventory: Regularly audit and query registry APIs for all dependencies, tracking package deprecation and advisories on vulnerabilities.
• Security Awareness: Developers using LLMs in their workflows should maintain awareness of these SSC-specific risks and integrate mitigation strategies at all entry points for automated code adoption.

In sum, SSCGuard advances both the scientific understanding and practical mitigation of software supply chain risks posed by LLM-generated code, offering a reproducible, scalable framework for empirical analysis, prompt- and middleware-based mitigation, and actionable recommendations for practitioners (Li et al., 24 Sep 2025).