Learning-Based Privacy-Preserving Graph Publishing against Sensitive Link Inference Attacks

Publishing graph data is widely desired to enable a variety of structural analyses and downstream tasks. However, it also potentially poses severe privacy leakage, as attackers may leverage the released graph data to launch attacks and precisely infer private information such as the existence of...

Enabling Cyber Security Education through Digital Twins and Generative AI

Digital Twins DTs are gaining prominence in cybersecurity for their ability to replicate complex IT Information Technology, OT Operational Technology, and IoT Internet of Things infrastructures, allowing for real time monitoring, threat analysis, and system simulation. This study investigates how...

SUSE CVE-2025-54121

Starlette is a lightweight ASGI Asynchronous Server Gateway Interface framework/toolkit, designed for building async web services in Python. In versions 0.47.1 and below, when parsing a multi-part form with large files greater than the default max spool size starlette will block the main thread t...

EX-NIDS: a Framework for Explainable Network Intrusion Detection Leveraging Large Language Models

This paper introduces eX-NIDS, a framework designed to enhance interpretability in flow-based Network Intrusion Detection Systems NIDS by leveraging Large Language Models LLMs. In our proposed framework, flows labelled as malicious by NIDS are initially processed through a module called the Promp...

Talking like a Phisher: LLM-Based Attacks on Voice Phishing Classifiers

Voice phishing vishing remains a persistent threat in cybersecurity, exploiting human trust through persuasive speech. While machine learning ML-based classifiers have shown promise in detecting malicious call transcripts, they remain vulnerable to adversarial manipulations that preserve semantic...

LLMxCPG: Context-Aware Vulnerability Detection through Code Property Graph-Guided Large Language Models

Software vulnerabilities present a persistent security challenge, with over 25,000 new vulnerabilities reported in the Common Vulnerabilities and Exposures CVE database in 2024 alone. While deep learning based approaches show promise for vulnerability detection, recent studies reveal critical...

Allocation of Resources Without Limits or Throttling

Overview starlette is a The little ASGI library that shines. Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling in the UploadFile.write method when handling multipart forms containing files larger than the default spoolmaxsize. An attacker can...

DEBIAN-CVE-2025-54121

Starlette is a lightweight ASGI Asynchronous Server Gateway Interface framework/toolkit, designed for building async web services in Python. In versions 0.47.1 and below, when parsing a multi-part form with large files greater than the default max spool size starlette will block the main thread t...

UBUNTU-CVE-2025-54121

Starlette is a lightweight ASGI Asynchronous Server Gateway Interface framework/toolkit, designed for building async web services in Python. In versions 0.47.1 and below, when parsing a multi-part form with large files greater than the default max spool size starlette will block the main thread t...

CVE-2025-54121 Starlette has possible denial-of-service vector when parsing large files in multipart forms

Starlette is a lightweight ASGI Asynchronous Server Gateway Interface framework/toolkit, designed for building async web services in Python. In versions 0.47.1 and below, when parsing a multi-part form with large files greater than the default max spool size starlette will block the main thread t...

Starlette has possible denial-of-service vector when parsing large files in multipart forms

Summary When parsing a multi-part form with large files greater than the default max spool size starlette will block the main thread to roll the file over to disk. This blocks the event thread which means we can't accept new connections. Details Please see this discussion for details:...

GHSA-2C2J-9GV5-CJ73 Starlette has possible denial-of-service vector when parsing large files in multipart forms

Summary When parsing a multi-part form with large files greater than the default max spool size starlette will block the main thread to roll the file over to disk. This blocks the event thread which means we can't accept new connections. Details Please see this discussion for details:...

How Search Engines, LLMs, and Third-Party Scrapers Affect Bot Management

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FaultLine: Automated Proof-Of-Vulnerability Generation Using LLM Agents

Despite the critical threat posed by software security vulnerabilities, reports are often incomplete, lacking the proof-of-vulnerability PoV tests needed to validate fixes and prevent regressions. These tests are crucial not only for ensuring patches work, but also for helping developers understa...

Chaindesk Cross Site Scripting

Chaindesk, a web application for constructing AI Agents, is vulnerable to a persistent cross site scripting vulnerability in its agent chat component. An attacker can achieve arbitrary client-side script execution by crafting an AI agent whose system prompt instructs the underlying Large Language...

PT-2025-30341

Name of the Vulnerable Software and Affected Versions Starlette versions 0.47.1 and below Description Starlette is a lightweight ASGI framework/toolkit for building async web services in Python. When parsing multi-part forms with large files exceeding the default maximum spool size, Starlette...

SynthCTI: LLM-Driven Synthetic CTI Generation to Enhance MITRE Technique Mapping

Cyber Threat Intelligence CTI mining involves extracting structured insights from unstructured threat data, enabling organizations to understand and respond to evolving adversarial behavior. A key task in CTI mining is mapping threat descriptions to MITRE ATT&CK techniques. However, this process...

CERT-UA Discovers LAMEHUG Malware Linked to APT28, Using LLM for Phishing Campaign

The Computer Emergency Response Team of Ukraine CERT-UA has disclosed details of a phishing campaign that's designed to deliver a malware codenamed LAMEHUG. "An obvious feature of LAMEHUG is the use of LLM large language model, used to generate commands based on their textual representation...

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques

Large Language Models LLMs are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in...

IDFace: Face Template Protection for Efficient and Secure Identification

As face recognition systems FRS become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteristics of the user's face image can be recovered from the template. Although recent advances in cryptographic tools such...