Oct 2026· Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering· 0 citations· 42 references
TL;DR
A defensive capability analysis for JavaScript libraries that soundly reports every exercised capability for code executed under a lightweight protected runtime, complementing the static analysis with a lightweight runtime enforcement mechanism that blocks those patterns.
Abstract
Capability analysis is a key building block for JavaScript supply-chain security tasks such as malware detection, security auditing, and runtime policy construction. However, existing approaches do not provide guarantees in the presence of JavaScript’s dynamic features. We present a defensive capability analysis for JavaScript libraries that soundly reports every exercised capability for code executed under a lightweight protected runtime. The analysis tracks only objects that are relevant to capabilities. It reports capability usage either at the program locations where such objects are used, or conservatively when relevant objects escape through operations that the analysis does not model precisely. To address a small number of dynamic language features that would otherwise invalidate this analysis, we complement the static analysis with a lightweight runtime enforcement mechanism that blocks those patterns. We implement the approach and evaluate it on large datasets of benign and malicious npm packages. The results show that the approach scales to real-world packages, pinpoints capability-usage locations with high accuracy, and remains compatible with 98.2% of benign libraries when using the protected runtime. Moreover, it detects capability usage in 99.8% of the malicious packages in our dataset, while the remaining cases are blocked by the runtime protection. Our study also shows that at least 72.9% of the npm packages use no security-sensitive capabilities at all, suggesting that capability analysis can substantially reduce auditing effort in practice.
A defensive capability analysis for JavaScript libraries that soundly reports every exercised capability for code executed under a lightweight protected runtime, complementing the static analysis with a lightweight runtime enforcement mechanism that blocks those patterns.
StackPatroller is proposed, a stack-integrity-based runtime supervision framework that enforces program integrity through configurable policies that monitors runtime stack contexts to detect early deviations from normal execution, enabling the identification of whole-function reuse and certain forward-edge attacks earl...
Yuanheng Xu, Si-Yu Zhang, Juan Wang et al.· Journal of computing and sec...· 0 citations
CWEEP can identify the exact location in the RTL where the potential vulnerability resides and supports automatic code repair suggestions when applicable, so it can be used in the early stages of RTL development while properties are still under construction.
A security assessment on 75 FastAPI backends generated by three contemporary LLMs revealed a disconnect between functional correctness and secure logic, which is interpreted as a review-risk pattern, which is called the human-in-the-loop paradox.
Abdul Ali Khan, S. Rauti, T. Mäkilä· IEEE Access· 0 citations
CHARGE is an automated framework for generating security properties for unverified RTL modules using CWEs and large language models using CWEs and large language models that leverages the hierarchical nature of CWE entries to improve accuracy when identifying security-critical assets in unverified RTL modules.
Experimental results show that AST-based structural features substantially improve recall compared with the TF-IDF baseline, while the combined TF-IDF and AST representation maintains this improved performance.
Vani Pasupula, M. N. V. Manikanth, Nagaraju Vassey· International Journal of Cre...· 0 citations
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