bitcoder-v2-research

BitCoder-v2 Research Organization: Bytepro AI Status: Active research — private repository Target: arXiv Q3 2026 + ARC Prize Paper Track Nov 2026 Research Objective Empirically document that the single-prompt ceiling observed in mathematical reasoning SAIR, arXiv:2504.18897 replicates in code...

CodeMimicry: Exploiting Safety Generalization Lag in Large Language Models Via Structured Code Completion

Large language models have achieved remarkable capabilities across diverse domains, yet their safety alignment remains vulnerable to jailbreak attacks. In this work, we identify a previously underexplored failure mode - safety generalization lag - where alignment trained predominantly on natural...

Benchmarking Neural Defend ARCAS 1B: A Foundational Multimodal Deepfake Detection Model

AI-generated imagery evolves faster than benchmark-specific detector evaluations, making a single score an incomplete account of generalization. This paper evaluates Neural Defend ARCAS 1B across benchmark families without benchmark-specific parameter updates. We retain native aggregation and...

Frequency-Domain Mixing Data Augmentation for Malicious Traffic Detection

The strong dynamics of network traffic often force malicious traffic detection models to handle out-of-distribution data. Typically, deep learning-based malicious traffic detection models require a large amount of high-quality training data. However, owing to challenges such as high labeling...

Evaluating ML-Based Intrusion Detection Systems: The Illusion of Model Efficacy

Intrusion Detection has been revolutionized due to the integration of Machine Learning. Improved detection rates, reduced false alarms, and optimized algorithms contribute to the perception of improved systems with optimal accuracy and near-perfect performance, the illusion of model efficacy...

Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection

Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Presentation attack detection PAD and morphing attack detection MAD are therefore essential components of trustworthy face biometrics. Despite...

Moirae: A Multimodal Agent Collaborative Framework for Dynamic Android Malware Detection

The Android ecosystem faces persistent and rapidly evolving malware threats. Existing machine learning detectors are vulnerable to concept drift because they rely on implementation-specific features whose distributions change over time. Large language models LLMs offer strong semantic understandi...

Cross-Corpus Evaluation of Generalizable Vulnerability Detection in IoT Firmware

IoT firmware vulnerability detection remains challenging due to heterogeneous firmware ecosystems, resource-constrained platforms, and limitations in existing benchmarks. Many datasets are synthetic or general-purpose and lack human-verified, contamination-screened annotations, limiting evidence ...

TGCM: Topic-Guided Generative Disentanglement of Interleaved APT Technique Sequences

In enterprise environments, multiple Advanced Persistent Threat APT campaigns often unfold concurrently, producing audit logs in which attack techniques across actors sources are interleaved over time. This setting naturally gives rise to an Unknown-K Interleaved Sequence Demixing UKISD problem:...

On the Study of Biometric Spoofing Detection Using Deep Learning

Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access. This research evaluates the effectiveness of state-of-the-art machine learning models,...

The Chronicles of Radio Frequency Fingerprinting

Radio Frequency Fingerprinting RFF has evolved from an early idea for radar emitter identification into a broad research field for wireless device identification and spectrum monitoring for security. Rather than presenting a conventional literature survey, this work provides a critical historical...

Learn from Your Mistakes: Tree-Like Self-Play for Secure Code LLMs

While Large Language Models LLMs excel in code generation, they remain prone to replicating subtle yet critical vulnerabilities endemic to their training data. Current alignment techniques, such as Supervised Fine-Tuning SFT and Reinforcement Learning RL, typically apply coarse-grained optimizati...

The Role of Domain-Specific Features in Malware Detection: A MacOS Case Study

Despite the growing popularity of macOS among end users and enterprise systems, malware research has primarily focused on Windows and Android operating systems, leaving the problem of macOS malware detection relatively unexplored. Indeed, the specificity of the operating system and the unique...

Token-Level Generalization in LoRA Adapter Backdoors: Attack Characterization and Behavioral Detection

We show that LoRA adapters, the dominant distribution format for fine-tuned LLMs, can be reliably backdoored through training data poisoning while preserving baseline task performance. On a Qwen 2.5 1.5B prompt-injection classifier, a small fraction of poisoned examples drives a...

PINSIGHT: A Comprehensive Threat Exploration of Domain-Adaptive Wi-Fi Based PIN Code Inference

Wi-Fi signals can be exploited by adversaries as a sensing side channel to eavesdrop on physical information. By monitoring propagation effects of radio waves within the victim's environment, attackers can remotely infer sensitive information. One particularly concerning example is PIN code...

HackerSignal: A Large-Scale Multi-Source Dataset Linking Hacker Community Discourse to the CVE Vulnerability Lifecycle

We introduce HackerSignal, a benchmark for temporal out-of-distribution cyber threat intelligence CTI and cross-source CVE linkage. HackerSignal aggregates 7.45 million exact-deduplicated documents from 64 public forum/source identifiers spanning eight source layers and a 36-year window 1990-2026...

MARD: A Multi-Agent Framework for Robust Android Malware Detection

With the rapid evolution of Android applications, traditional machine learning-based detection models suffer from concept drift. Additionally, they are constrained by shallow features, lacking deep semantic understanding and interpretability of decisions. Although Large Language Models LLMs...

A Systematic Literature Review for Transformer-Based Software Vulnerability Detection

Context: Software vulnerabilities pose significant security threats to software systems, especially as software is increasingly used across many areas of daily life, including health, government, and finance. Recently, transformer-based models have demonstrated promising results in automatic...

ID-Eraser: Proactive Defense against Face Swapping Via Identity Perturbation

Deepfake technologies have rapidly advanced with modern generative AI, and face swapping in particular poses serious threats to privacy and digital security. Existing proactive defenses mostly rely on pixel-level perturbations, which are ineffective against contemporary swapping models that extra...

MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security

Network security is a critical concern in the digital landscape of today, with users demanding secure browsing experiences and protection of their personal data. This study explores the dynamic integration of Machine Learning ML algorithms with Software-Defined Networking SDN controllers to enhan...