Where Do the Tokens Go? Understanding and Reducing Costs in LLM Agents for Vulnerability Discovery
LLM agents can spend millions of tokens during vulnerability discovery without producing a working proof of concept (PoC
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Effective source recovery is critical to security applications such as malware analysis, vulnerability assessment, and l
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Large language models (LLMs) have shown strong potential for automated vulnerability patching, but current benchmarks ca
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In post-deployment time, inputs to deep learning models may or may not be adversarially patched. Patch robustness certif
Research software sustainability is often assessed through the FAIR principles: findability, accessibility, interoperabi
Agent skills combine instructions with executable resources, giving third-party packages access to an agent's runtime. E
As small language models (SLMs) are increasingly deployed on resource-constrained and on-device platforms, including as
Large Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depen
Large Language Models (LLMs) are widely used to generate code. Although their functional plausibility keeps improving, t
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Static-analysis checker synthesis requires agents to interpret a defect specification, inspect a repository, implement a