4 citations · 6 across the 12 of their papers we have counts for
14 papers
Kairos: Transactional Security Enforcement at the System Boundary for LLM Agents
Kristoffer Hempel, Nils Loose, Lorenzo Cavallaro +1
Large language model (LLM) agents are becoming an integral part of the software development lifecycle, and the capabilities granted to them allow an agent to act with the user's au…
U-Space: Uncovering When and Why Uncertainty Arises in Language Models
Tobias Braun, Nils Loose, Alexander Herzog +4
Large language models are informing decisions with ever-higher stakes. As the consequences of their errors grow, a central question becomes harder to ignore: how much can we trust…
Antaeus: Hunting Repository-Level Logic Vulnerabilities via Context-Grounded LLM Reasoning
Michele Armillotta, Nicolò Romandini, Rebecca Montanari +1
LLM-based vulnerability detectors have shown promising results in identifying memory-safety bugs and vulnerability classes whose violations can often be expressed through establish…
Poster: Rethinking Security in LLM Code Generation through Real-World Risk Scenarios
Lixun Ma, Ruolong Ma, Bei Wang +4
Large Language Models (LLMs) are widely used for code generation, yet their security behavior in realistic development workflows remains underexplored. Existing benchmarks often re…
Malaika: Understanding Malware through Tri-Grounded Agentic Reasoning
Xingzhi Qian, Xinran Zheng, Yiling He +1
Recent LLM-based systems have shown promising capabilities for security-focused code analysis. Malware understanding, however, poses a distinct challenge: analysts must reconstruct…
Demystifying the Mythos or Disrupting Bugonomics? From Zero-Day Asymmetry to Defender Remediation Throughput
Alfredo Pesoli, Herman Errico, Lorenzo Cavallaro
Recent demonstrations of large language models producing candidate and confirmed vulnerabilities in production software have renewed the narrative that AI will reshape offensive an…
Reading Calibrated Uncertainty from Language Model Trajectories
Aliai Eusebi, Alexander Herzog, Xiaoyu Liang +3
The maximum softmax probability (MSP) represents a default approach when evaluating uncertainty quantification for language model generation with structured output. Although cheap,…
Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries
Xinran Zheng, Alfredo Pesoli, Marco Valleri +2
Frontier LLM agents are increasingly capable of localizing suspicious code, but vulnerability reasoning requires more than access to program artifacts. An agent must carry forward…
Retrofit: Continual Learning with Controlled Forgetting for Binary Security Detection and Analysis
Yiling He, Junchi Lei, Hongyu She +5
Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics. However, the performance often degrades as threat landscapes evolve…
Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing
Xinran Zheng, Xingzhi Qian, Yiling He +2
Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them wi…
Learning to Focus: Context Extraction for Efficient Code Vulnerability Detection with Language Models
Xinran Zheng, Xingzhi Qian, Huichi Zhou +4
Language models (LMs) show promise for vulnerability detection but struggle with long, real-world code due to sparse and uncertain vulnerability locations. These issues, exacerbate…
On Benchmarking Code LLMs for Android Malware Analysis
Yiling He, Hongyu She, Xingzhi Qian +4
Large Language Models (LLMs) have demonstrated strong capabilities in various code intelligence tasks. However, their effectiveness for Android malware analysis remains underexplor…
LAMD: Context-driven Android Malware Detection and Classification with LLMs
Xingzhi Qian, Xinran Zheng, Yiling He +2
The rapid growth of mobile applications has escalated Android malware threats. Although there are numerous detection methods, they often struggle with evolving attacks, dataset bia…
TIF: Learning Temporal Invariance in Android Malware Detectors
Xinran Zheng, Shuo Yang, Edith C. H. Ngai +2
Learning-based Android malware detectors degrade over time due to natural distribution drift caused by malware variants and new families. This paper systematically investigates the…