Data Encryption Battlefield: a Deep Dive into the Dynamic Confrontations in Ransomware Attacks

In the rapidly evolving landscape of cybersecurity threats, ransomware represents a significant challenge. Attackers increasingly employ sophisticated encryption methods, such as entropy reduction through Base64 encoding, and partial or intermittent encryption to evade traditional detection...

Phishing URL Detection Using Bi-LSTM

Phishing attacks threaten online users, often leading to data breaches, financial losses, and identity theft. Traditional phishing detection systems struggle with high false positive rates and are usually limited by the types of attacks they can identify. This paper proposes a deep learning-based...

JailbreaksOverTime: Detecting Jailbreak Attacks under Distribution Shift

Safety and security remain critical concerns in AI deployment. Despite safety training through reinforcement learning with human feedback RLHF 32, language models remain vulnerable to jailbreak attacks that bypass safety guardrails. Universal jailbreaks - prefixes that can circumvent alignment fo...

CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenges

Large language models LLMs have demonstrated remarkable capabilities, especially the recent advancements in reasoning, such as o1 and o3, pushing the boundaries of AI. Despite these impressive achievements in mathematics and coding, the reasoning abilities of LLMs in domains requiring cryptograph...

A Gradient-Optimized TSK Fuzzy Framework for Explainable Phishing Detection

Phishing attacks represent an increasingly sophisticated and pervasive threat to individuals and organizations, causing significant financial losses, identity theft, and severe damage to institutional reputations. Existing phishing detection methods often struggle to simultaneously achieve high...

Optimized Approaches to Malware Detection: a Study of Machine Learning and Deep Learning Techniques

Digital systems find it challenging to keep up with cybersecurity threats. The daily emergence of more than 560,000 new malware strains poses significant hazards to the digital ecosystem. The traditional malware detection methods fail to operate properly and yield high false positive rates with l...

Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions

Phishing detection on Ethereum has increasingly leveraged advanced machine learning techniques to identify fraudulent transactions. However, limited attention has been given to understanding the effectiveness of feature selection strategies and the role of graph-based models in enhancing detectio...

Integrating Graph Theoretical Approaches in Cybersecurity Education CSCI-RTED

As cybersecurity threats continue to evolve, the need for advanced tools to analyze and understand complex cyber environments has become increasingly critical. Graph theory offers a powerful framework for modeling relationships within cyber ecosystems, making it highly applicable to cybersecurity...

Automatically Generating Rules of Malicious Software Packages Via Large Language Model

Today's security tools predominantly rely on predefined rules crafted by experts, making them poorly adapted to the emergence of software supply chain attacks. To tackle this limitation, we propose a novel tool, RuleLLM, which leverages large language models LLMs to automate rule generation for O...

C2RUST-BENCH: a Minimized, Representative Dataset for C-To-Rust Transpilation Evaluation

Despite the effort in vulnerability detection over the last two decades, memory safety vulnerabilities continue to be a critical problem. Recent reports suggest that the key solution is to migrate to memory-safe languages. To this end, C-to-Rust transpilation becomes popular to resolve...

Trace Gadgets: Minimizing Code Context for Machine Learning-Based Vulnerability Prediction

As the number of web applications and API endpoints exposed to the Internet continues to grow, so does the number of exploitable vulnerabilities. Manually identifying such vulnerabilities is tedious. Meanwhile, static security scanners tend to produce many false positives. While machine...

Everything You Wanted to Know about LLM-Based Vulnerability Detection but Were Afraid to Ask

Large Language Models are a promising tool for automated vulnerability detection, thanks to their success in code generation and repair. However, despite widespread adoption, a critical question remains: Are LLMs truly effective at detecting real-world vulnerabilities? Current evaluations, which...

MULTI-LF: a Unified Continuous Learning Framework for Real-Time DDoS Detection in Multi-Environment Networks

Detecting Distributed Denial of Service DDoS attacks in Multi-Environment M-En networks presents significant challenges due to diverse malicious traffic patterns and the evolving nature of cyber threats. Existing AI-based detection systems struggle to adapt to new attack strategies and lack...

X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents

Multi-turn interactions with language models LMs pose critical safety risks, as harmful intent can be strategically spread across exchanges. Yet, the vast majority of prior work has focused on single-turn safety, while adaptability and diversity remain among the key challenges of multi-turn...

CVE-2024-10272

lunary-ai/lunary is vulnerable to broken access control in the latest version. An attacker can view the content of any dataset without any kind of authorization by sending a GET request to the /v1/datasets endpoint without a valid authorization token...

Arbitrary File Write via Archive Extraction (Zip Slip)

Overview gluoncv is a Gluon CV Toolkit Affected versions of this package are vulnerable to Arbitrary File Write via Archive Extraction Zip Slip via the fromcsv function in ImageClassificationDataset. An attacker can overwrite files on the victim's system by using this function to extract maliciou...

CVE-2024-10707

gaizhenbiao/chuanhuchatgpt version git d4ec6a3 is affected by a local file inclusion vulnerability due to the use of the gradio component gr.JSON, which has a known issue CVE-2024-4941. This vulnerability allows unauthenticated users to access arbitrary files on the server by uploading a speciall...

PYSEC-2025-93

gaizhenbiao/chuanhuchatgpt version git d4ec6a3 is affected by a local file inclusion vulnerability due to the use of the gradio component gr.JSON, which has a known issue CVE-2024-4941. This vulnerability allows unauthenticated users to access arbitrary files on the server by uploading a speciall...

PYSEC-2025-93

gaizhenbiao/chuanhuchatgpt version git d4ec6a3 is affected by a local file inclusion vulnerability due to the use of the gradio component gr.JSON, which has a known issue CVE-2024-4941. This vulnerability allows unauthenticated users to access arbitrary files on the server by uploading a speciall...

CVE-2024-10272

lunary-ai/lunary is vulnerable to broken access control in the latest version. An attacker can view the content of any dataset without any kind of authorization by sending a GET request to the /v1/datasets endpoint without a valid authorization token...