CuraSec

tag: Static-Analysis · 4 items

Learn ARQ: LLM-based framework auto-refines CodeQL queries for C/C++
2026-08-24 · arXiv cs.CR · source ↗ #static-analysis#codeql#ai-security
  • Engineer — Learn: Research showing an LLM-driven refinement loop can cut false positives and grow true positive rates by up to ~120% in CodeQL C/C++ queries without labeled datasets — worth tracking if your AppSec pipeline relies on CodeQL, but no action needed on running systems today.
  • SOC/IR — Skip
  • Leader — Skip
Learn archived LLM + Code Slicing for NFT Smart Contract Vulnerability Detection
2026-07-27 · arXiv cs.CR · source ↗ #smart-contracts#llm-security#static-analysis
  • Engineer — Learn: Interesting research combining code slicing with LLM analysis to detect reentrancy and overflow in ERC-721 contracts, but no tooling release or actionable change to running systems today.
  • SOC/IR — Skip
  • Leader — Skip
Learn archived Tsetlin Machine Framework for Interpretable PDF Malware Detection
2026-07-13 · arXiv cs.CR · source ↗ #pdf-malware#machine-learning#static-analysis
  • Engineer — Learn: Academic proposal for interpretable static PDF analysis using Tsetlin Machines; no tooling released or integrated into common pipelines, but the interpretability angle is worth tracking for teams building or evaluating ML-based malware classifiers.
  • SOC/IR — Learn: The interpretability feature could eventually improve analyst trust in ML-based PDF triage, but no detection rules, IOCs, or deployable tooling accompany this research paper.
  • Leader — Skip
Learn archived Malaika: LLM Multi-Agent Framework for Android Malware Behavior Analysis
2026-07-13 · arXiv cs.CR · source ↗ #malware-analysis#llm-security#static-analysis
  • Engineer — Learn: Academic research on grounded agentic reasoning for malware behavior reconstruction; no immediate engineering action, but the tri-grounding approach (domain, semantics, knowledge) is worth noting when evaluating LLM-assisted code-analysis tooling.
  • SOC/IR — Learn: Malaika’s behavior-reconstruction framing — connecting sparse program evidence to auditable behavioral conclusions — could inform how teams structure LLM-assisted malware triage workflows, though no detection or hunt action is available from this paper alone.
  • Leader — Skip