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Found 5 papers, 0 papers with code
Do Large Code Models Understand Programming Concepts? A Black-box Approach
Large Language Models' success on text generation has also made them better at code generation and coding tasks.
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations
Highlighting volatile information channels within the software, we introduce three software pre-processing steps to eliminate the attack surface, namely, padding removal, software stripping, and inter-section information resetting.
Identifying and Mitigating the Security Risks of Generative AI
However, GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks.
Robust and Accurate Authorship Attribution via Program Normalization
To address these emerging issues, we formulate this security challenge into a general threat model, the $\textit{relational adversary}$, that allows an arbitrary number of the semantics-preserving transformations to be applied to an input in any problem space.
COSET: A Benchmark for Evaluating Neural Program Embeddings
Neural program embedding can be helpful in analyzing large software, a task that is challenging for traditional logic-based program analyses due to their limited scalability.
