LLMSecConfig: Automating Security Misconfiguration Repair in Kubernetes

Introduction & Motivation

Container orchestrators, most notably Kubernetes, have become a cornerstone of modern cloud‑native infrastructures. Their declarative configuration model enables rapid deployment, scaling, and management of distributed applications. However, the same expressiveness that delivers operational agility also exposes a wide attack surface. Security misconfigurations—incorrectly set pod security policies, exposed ports, or permissive service accounts—are the leading cause of vulnerabilities in containerised environments. These misconfigurations often arise from the sheer volume and complexity of configuration files, the need to coordinate across multiple namespaces, and the hierarchical nature of Kubernetes objects.

Current practice relies heavily on static analysis tools (SATs) such as Checkov to surface misconfigurations. SATs excel at detecting violations of best‑practice policies, producing concise error messages, policy identifiers, and severity scores. Yet they stop short of providing automated remediation. In a CI/CD pipeline, a developer is expected to manually interpret the SAT output and edit the corresponding YAML manifests. This manual step is error‑prone, introduces variability, and does not scale as the number of manifests grows or as teams iterate rapidly.

The challenge is compounded by the interdependencies inherent in Kubernetes configurations. A change to a pod definition can cascade to services, deployments, and network policies, potentially breaking application functionality or creating new security gaps. Manual fixes, especially when performed in isolation or without a holistic view of the cluster state, can inadvertently introduce new vulnerabilities or operational failures. Consequently, a single mis‑edit may propagate through the deployment pipeline, leading to costly rollbacks or security incidents.

There is a clear research gap: an automated repair system that can ingest SAT output, understand the context of each violation, and generate a corrective patch that preserves the intended operational behaviour. Such a system would reduce the cognitive load on developers, ensure consistent remediation across environments, and accelerate security hardening in dynamic DevOps workflows. The LLMSecConfig framework addresses this gap by integrating SATs with large language models and retrieval‑augmented generation, providing a principled pipeline that automatically repairs misconfigurations while rigorously validating each fix.

LLMSecConfig Architecture

LLMSecConfig follows a tightly coupled, three‑phase pipeline that orchestrates static analysis, retrieval‑augmented generation, and rigorous validation. Each phase feeds deterministic, verifiable artefacts into the next, ensuring that the final output is both syntactically correct and security‑compliant.

1. SAT Integration

The pipeline begins with the Static Analysis Tool (SAT) component, which employs Checkov to scan a Kubernetes manifest. Checkov is chosen for its extensive policy library (>1,000 built‑in rules), pure‑Python implementation, and comprehensive Prisma Cloud documentation. The SAT emits a structured report that captures the following for every detected issue:

• Policy identifier (e.g., CKV_K8S_1),
• Severity level,
• Error message detailing the misconfiguration, and
• Reference URLs that link to Checkov documentation and Prisma Cloud guidelines.
  This report serves as the primary trigger for the subsequent context assembly.

2. Context Retrieval (RAG)

Once a misconfiguration is identified, the system aggregates contextual data to inform the LLM. The retrieval module pulls:

1. Checkov policy source code — the exact Python implementation that defines the rule. The LLM can inspect the logic that led to the violation and understand the required structural changes.
2. Prisma Cloud documentation — policy descriptions, recommended remediation steps, and potential side‑effects. This narrative layer provides human‑readable guidance that the LLM can reference when crafting a patch.
3. The raw manifest snippet — the YAML fragment where the issue resides, so the model can anchor the proposed changes precisely.
   The aggregated context is formatted into a prompt template that exposes all three elements in a consistent, machine‑readable layout. The prompt explicitly asks the LLM to produce a YAML patch that resolves the misconfiguration while preserving the surrounding configuration semantics.

3. Repair Generation & Validation

The LLM (either MistralLarge2 or GPT‑4o mini) receives the prompt and generates a patch. Validation is performed iteratively:

• Syntactic check: The patch is passed back through Checkov to ensure it is valid YAML and does not introduce new syntax errors.
• Security re‑scan: The patched manifest is rescanned with Checkov. If any of the original issues persist, the LLM is invoked again with the updated context, including the previous patch attempt.
• Retry limits: The system enforces a maximum of 10 parser retries and 5 overall attempts per issue. Once a patch passes all checks or the retry budget is exhausted, the iteration ends.

Throughout this process, detailed logs are captured: the original manifest, each generated patch, the SAT output before and after the patch, and the success/failure status of each validation step. This logging infrastructure guarantees auditability and reproducibility, allowing developers to trace every change back to the underlying policy logic and documentation.

By coupling SAT‑driven detection with retrieval‑augmented generation and multi‑round validation, LLMSecConfig delivers automated, reliable repairs that maintain operational integrity while eliminating security misconfigurations across thousands of Kubernetes manifests.

Evaluation & Results

The evaluation of LLMSecConfig focuses on three dimensions: the real‑world scale of the dataset, the comparative performance of different LLM back‑ends, and a set of quantitative metrics that capture repair quality, reliability, and security impact.

Dataset and Experimental Setup

The benchmark comprises 1,000 Kubernetes manifests extracted from ArtifactHub and filtered to retain only configurations that triggered at least one Checkov misconfiguration warning. Each manifest spans the full hierarchy from cluster to container, providing a diverse set of security policies. The experimental pipeline is identical for all models: Checkov first enumerates issues, the context retrieval stage assembles policy‑specific information (policy ID, error message, source code, and Prisma Cloud documentation), and the LLM generates a YAML patch. The patch is then validated by a second round of Checkov scans; if the patch fails to resolve all issues, the LLM receives the new validation feedback and retries until either all issues are cleared or a retry limit of five is reached.

The LLMs are configured with a temperature of 0.5 to balance creativity and determinism, a maximum parser retry of 10 to allow for syntactic adjustments, and a maximum retry of 5 to limit the number of repair attempts per file.

Models Compared

Two large‑language models were evaluated:

• Mistral Large 2 — a 12‑billion‑parameter model trained on a broad mixture of code‑centric data.
• GPT‑4o mini — a 3‑billion‑parameter variant of OpenAI’s GPT‑4o, optimized for cost‑effective inference.
  Both models received the same contextual prompt and were evaluated on the identical dataset.

Key Metrics

The following table summarizes the performance of each model across five metrics that capture different aspects of repair quality.

Metric GPT‑4o mini Mistral Large 2 Pass Rate (PR) 40.2 % 94.3 % Parse Success Rate (PSR) 99.8 % 100 % Average Pass Steps (APS) 4.38 3.06 AUC‑PRS 0.241 0.696 AUC‑APSS 2.495 2.249 Security Improvement 0.986 0.986 Avg. Introduced Errors 0.029 0.024

Pass Rate measures the proportion of manifests that were fully repaired. Parse Success Rate reflects the fraction of generated patches that were syntactically valid YAML. Average Pass Steps indicates how many repair attempts, on average, were required to achieve a passing configuration. AUC‑PRS and AUC‑APSS aggregate performance across the retry spectrum, capturing both success likelihood and efficiency. Security Improvement denotes the reduction in Checkov severity scores after repair, while Avg. Introduced Errors counts any new misconfigurations that appeared during the repair process.

Findings

Mistral Large 2 outperforms GPT‑4o mini on every metric. Its 94.3 % pass rate is more than twice the performance of GPT‑4o mini, and its parse success rate reaches 100 %, indicating that every patch it generates is syntactically correct. The lower average pass steps (3.06 vs. 4.38) shows that Mistral requires fewer iterations to converge on a valid repair. The AUC‑metrics corroborate this trend, with Mistral’s values more than double those of GPT‑4o mini.

Both models achieve the same security improvement of 0.986, demonstrating that when repairs succeed, they are equally effective at eliminating the original misconfigurations. The average introduced errors are negligible for both models, with Mistral slightly ahead (0.024 vs. 0.029). These results confirm that the repair pipeline does not inadvertently create new vulnerabilities.

In summary, the experimental evidence establishes that LLMSecConfig with Mistral Large 2 provides a robust, high‑throughput solution for automated Kubernetes misconfiguration repair, achieving near‑perfect syntactic success and a markedly higher repair success rate than the GPT‑4o mini baseline.

Contextual Ablation Study

The ablation study evaluates how different sources of context influence the LLM’s ability to repair Kubernetes misconfigurations. All experiments were conducted with the Mistral Large 2 model, as it was the best performing architecture in the overall evaluation. The study systematically varied the prompt payload across four configurations:

1. Checkov Output only — The prompt contains the raw SAT diagnostics (policy ID, severity, error message, and links) generated by Checkov.
2. Checkov Output + Source Code — In addition to the SAT diagnostics, the prompt includes the full Python implementation of the Checkov policy that identified the issue.
3. Checkov Output + Prisma Docs — The prompt augments the SAT diagnostics with the relevant section from Prisma Cloud’s documentation for the policy.
4. Full Context (output + code + docs) — The most comprehensive prompt, providing SAT diagnostics, policy source code, and Prisma documentation.

For each configuration, the LLM was tasked with producing a minimal YAML patch that resolves the reported misconfiguration. The repair pipeline then iterated: the patch was applied, syntax was re‑validated with the SAT engine, and the configuration was rescanned for residual issues. The process repeated until either all issues were cleared or the retry limit was reached.

Quantitative Results

Context Pass Rate (PR) Average Pass Steps (APS) Checkov Output only 88 % 4.38 Checkov Output + Source Code 90.3 % 2.68 Checkov Output + Prisma Docs 65.2 % 4.00 Full Context (output + code + docs) 94.3 % 3.06

The Pass Rate measures the proportion of input manifests for which the system achieved a fully compliant output. The Average Pass Steps counts the number of LLM iterations required before the final patch succeeded. The baseline (Checkov only) already achieved a respectable 88 % pass rate, indicating that the SAT diagnostics provide sufficient guidance for many repairs. However, adding the policy source code improved the pass rate by 2.3 % and reduced the number of required iterations by more than 30 %, underscoring the value of exposing the LLM to the exact logic the SAT uses to flag the issue.

Contrastingly, incorporating Prisma documentation alone degraded performance, yielding a 29 % drop in pass rate relative to the baseline. The documentation, while authoritative, contains broader explanations and optional recommendations, which likely introduced noise that the LLM had to disentangle. When the documentation was combined with the other two sources, the overall performance improved to the best level observed (94.3 % PR) but the benefit over the source‑code‑only configuration was modest (≈ 4 % absolute improvement). The APS metric mirrored this pattern: the full context required slightly fewer iterations than source‑code‑only, suggesting that the documentation helped the LLM converge faster in a minority of cases.

Qualitative Insight

The ablation demonstrates that policy source code is the most informative context. It provides the LLM with concrete implementation details—exact field names, conditional logic, and accepted values—allowing it to generate precise patches. The SAT output alone supplies the high‑level problem description, but the LLM must infer the underlying structure, which it does well enough for many cases but less reliably for complex policies.

Prisma documentation, while useful for human readers, is less well suited to prompt engineering in this setting. Its prose style and broader scope can mislead the LLM into over‑generalizing or incorporating irrelevant suggestions. When paired with code, the documentation appears to offer marginal guidance—perhaps reinforcing the correct terminology—but does not substantially shift the repair trajectory.

Overall, the study confirms that a minimal but targeted context—SAT diagnostics plus policy source code—strikes a favorable balance between informativeness and prompt length. Adding extensive documentation yields a small performance bump at the cost of additional prompt tokens and potential noise. These findings inform the design of future prompt templates and underline the importance of exposing the LLM to the very code that identifies the vulnerability.

Future Work & Takeaways

The LLMSecConfig project opens several avenues for further research and practical deployment:

1. Generalisation to Other Orchestrators — The current implementation targets Kubernetes. Extending the framework to Docker Swarm, Nomad, or Cloud‑provider‑specific orchestration services would broaden its applicability.
2. Hybrid Repair Strategies — Combining LLM‑generated patches with rule‑based post‑processing could reduce false positives and enforce stricter semantic constraints.
3. Adaptive Retry Mechanisms — Learning from past repair failures to adjust prompt wording or retry limits dynamically could further improve efficiency.
4. User‑Centric Evaluation — Conducting studies with DevOps teams to assess usability, integration into existing CI/CD pipelines, and the overall impact on security posture.
5. Open‑Source Release — The full implementation, dataset, and evaluation scripts are available on Figshare (https://figshare.com/s/2a9be8ccfbec9d8ba199). Researchers and practitioners can reproduce results, contribute improvements, and explore new LLM back‑ends.

Takeaway for Practitioners

Automated repair of container orchestrator misconfigurations is feasible and highly effective when built on a solid foundation of static analysis, contextual retrieval, and iterative validation. LLMSecConfig demonstrates that a carefully engineered prompt—particularly one that includes the SAT diagnostics and the source code of the violated policy—enables large language models to produce reliable, syntactically correct patches with minimal human intervention.

By integrating such a system into CI/CD workflows, teams can achieve continuous security hardening without manual effort, reducing the risk of configuration‑related vulnerabilities and accelerating deployment cycles.

Conclusion

LLMSecConfig represents a significant step forward in the quest for secure, automated deployment pipelines. By marrying static analysis with large language models and a rigorous validation loop, we provide a practical, open‑source tool that repairs Kubernetes misconfigurations with high accuracy and low overhead. Our extensive evaluation, including a contextual ablation study, offers actionable insights for future work and for practitioners looking to safeguard their cloud‑native environments.

We invite the community to experiment with the framework, contribute improvements, and explore its extension to other domains. Together, we can bring the power of large language models to the front lines of infrastructure security.