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.
Abstract
As the hardware layer becomes a focus point for attackers, the need for improved hardware security verification techniques is more important than ever. State-of-the-art security verification techniques require significant manual effort from individuals with security expertise. Furthermore, there is no standard method to locate where the fault lies within the register transfer level (RTL) code. This paper presents CWEEP, a static analysis framework for detecting security weaknesses in RTL. CWEEP does not require a detailed security specification, so it can be used in the early stages of RTL development while properties are still under construction. Furthermore, CWEEP can identify the exact location in the RTL where the potential vulnerability resides and supports automatic code repair suggestions when applicable. Using datasets from the literature, we evaluate the performance of CWEEP on a set of two SoC designs with manually inserted bugs and on a large language model generated dataset, consisting of 3874 buggy modules. We find that CWEEP issues a correct warning up to 60.8% of the time. In contrast, the tool from a previous work issued a correct warning 17.5% of the time for the same dataset.
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.
Differential fuzzing reveals complementary bugs in the manual and LLM porting of security-critical C libraries to idiomatic Rust and translates these findings into concrete practical guidance for teams planning a similar migration.
Marco Parrillo, Marco Grassi, Luigi Laura· Future Internet· 0 citations
Vulnerability detection via static analysis traditionally relies on security experts encoding insecure coding patterns into algorithmic rules. However, this approach often focuses on syntactic patterns and overlooks deeper semantic information in the code, such as the meanings of variable and function names. As software systems grow more complex, modeling vulnerabilities using only syntactic rules becomes increasingly challenging. In this paper, we propose a semantics-aware approach to detecting software vulnerabilities. We present SETYPE, a semantics-aware type system that can be derived directly from source code based solely on the meanings of symbols and expressions in natural language. In the SETYPE type system, both type inference and checking are performed by Large Language Models (LLMs), and a failed type check indicates a potential vulnerability. We prototype PYSETYPE to demonstrate the feasibility of SETYPE for detecting vulnerabilities in Python web applications. Our evaluation on real-world applications achieves 87% detection precision and 88% detection accuracy. Using PYSETYPE, we identified 15 potential zero-day vulnerabilities, nine of which were confirmed by developers.
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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