The application of LLMs for detecting malicious PowerShell scripts and producing human-interpretable explanations for their classification decisions are investigated, showing that LLMs are capable of identifying and explaining malicious PowerShell scripts, although performance varies across different models.
Script-based malware remains a prevalent attack technique. These scripts often contain indicators of compromise (IOCs) that provide actionable threat intelligence. However, statically recovering such indicators is challenging, as relevant values may be dispersed or transformed within code. Although large language models (LLMs) have shown promise in security analysis, their ability to recover IOCs from malicious scripts remains underexplored. We present SCRIPTIOC-BENCH, a benchmark for measuring static IOC extraction capability on real-world malicious scripts. The benchmark comprises 634 manually verified JavaScript, PowerShell, and VBScript malware samples covering four IOC types (URLs, domains, IP addresses, and filesystem artifacts). We further stratify ground-truth IOCs by recovery level, distinguishing directly exposed indicators from those requiring decoding or reconstruction. Using this benchmark, we evaluate a broad range of proprietary and open-weight LLMs and show that IOC recovery without execution remains challenging across model scales: the strongest model reaches only 65.4 F1. To characterize how recovery fails, we introduce a false-positive taxonomy and use it to compare the error profiles of the evaluated models. We further study two mitigations on a small open-weight model, deterministic string utilities and task-specific adaptation, finding that they provide complementary recovery gains, raise precision, and shift errors toward sample-grounded mismatches.
Hanna Kim, Jian Cui, Minkyoo Song et al.· 0 citations
Modern malware frameworks use advanced evasion techniques that bypass traditional detection methods; thus entail advanced analytical frameworks for comprehensive and robust analysis. This study provides a comparative analysis of several frameworks that utilize Explainable Artificial Intelligence (XAI), Generative Adversarial Networks (GANs), and Large Language Models (LLMs) to provide a dynamic approach to malware behaviour. The review consisted of five key metrics to assess these three frameworks: detection performance, explainability, robustness against adversarial attacks, behavioral interpretation, and automated reporting capabilities. The results indicate that Deep Learning models have attained high accuracy in detect-ing and identifying malicious code, but are not interpretable. The GAN-based frameworks are highly effective in generating adversarial samples for robustness testing. Conversely, LLM-based approaches are highly effective for generating automated forensic reports, but are not yet fully integrated into malware detection workflows. The analysis highlights a research gap pertaining to the lack of integrated frameworks for adversarial analysis, explainability and automated forensic reporting. This study proposes a unified approach of malware analysis incorpo-rating XAI, GAN and LLM to enable the development of more effective malware detection tools with more transparency, deeper analytical insight and advanced forensic decision-making.
A. Verma, Neha Gupta, Akash Saxena et al.· International Journal of Inn...· 0 citations
Command injection vulnerabilities remain a significant security threat in dynamic languages such as Python, particularly in widely used open-source projects. Recent advances in large language models (LLMs) have shown strong potential in code-related tasks, motivating their application to vulnerability detection.In this paper, we evaluate LLM-based approaches for detecting command injection vulnerabilities and generating security tests. We apply our method to six widely used GitHub projects—Django, Flask, TensorFlow, Scikit-learn, PyTorch, and LangChain—covering over 50,000-star repositories with broad real-world usage. Our study analyzes the effectiveness of LLMs in terms of detection accuracy, efficiency, and practical applicability.We further compare multiple LLMs to assess their relative performance in vulnerability detection and test generation. The results demonstrate that LLM-based approaches can effectively identify vulnerabilities in both complete and fragmented code while providing executable security tests for validation. Our findings highlight the strengths and limitations of LLMs and offer practical insights for leveraging them to enhance automated software security testing.
Large language models (LLMs) are increasingly used for code generation, yet they remain vulnerable to prompts that elicit insecure implementations. Existing defenses typically rely on predefined threat models or known vulnerability patterns, limiting their effectiveness against novel attacks. We propose CodeSIFT, a threat-model-agnostic detection method that leverages influence functions to identify batches of prompts that induce anomalous model behavior. Rather than detecting specific vulnerabilities, CodeSIFT measures the parameter-space influence of generated code and uses a statistical test to determine whether a candidate prompt set deviates from a benign reference distribution. To evaluate our approach, we introduce two benchmark datasets covering a variety of vulnerabilities. We evaluate CodeSIFT on three open-weight code LLMs ranging from 3B to 7B parameters, achieving AUROC scores of up to 0.98 at moderate-to-high injection rates, while maintaining well-calibrated false positive rates and substantially outperforming static analysis baselines. These results suggest that influence-function-based detection is a promising direction for identifying malicious code-generation prompts without requiring prior knowledge of the underlying attack class.
Francesco Quinzan, Noor Munir, Yi-Shun Lu et al.· 0 citations
