Protocol-Agnostic and Data-Free Backdoor Attacks on Pre-Trained Models in RF Fingerprinting

While supervised deep neural networks DNNs have proven effective for device authentication via radio frequency RF fingerprinting, they are hindered by domain shift issues and the scarcity of labeled data. The success of large language models has led to increased interest in unsupervised pre-train...

Analysis of the Vulnerability of Machine Learning Regression Models to Adversarial Attacks Using Data from 5G Wireless Networks

This article describes the process of creating a script and conducting an analytical study of a dataset using the DeepMIMO emulator. An advertorial attack was carried out using the FGSM method to maximize the gradient. A comparison is made of the effectiveness of binary classifiers in the task of...

OET: Optimization-Based Prompt Injection Evaluation Toolkit

Large Language Models LLMs have demonstrated remarkable capabilities in natural language understanding and generation, enabling their widespread adoption across various domains. However, their susceptibility to prompt injection attacks poses significant security risks, as adversarial inputs can...

From Texts to Shields: Convergence of Large Language Models and Cybersecurity

This report explores the convergence of large language models LLMs and cybersecurity, synthesizing interdisciplinary insights from network security, artificial intelligence, formal methods, and human-centered design. It examines emerging applications of LLMs in software and network security, 5G...

Can Differentially Private Fine-Tuning LLMs Protect against Privacy Attacks?

Fine-tuning large language models LLMs has become an essential strategy for adapting them to specialized tasks; however, this process introduces significant privacy challenges, as sensitive training data may be inadvertently memorized and exposed. Although differential privacy DP offers strong...

Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services

Due to the increasing number of tasks that are solved on remote servers, identifying and classifying traffic is an important task to reduce the load on the server. There are various methods for classifying traffic. This paper discusses machine learning models for solving this problem. However, su...

Hoist with His Own Petard: Inducing Guardrails to Facilitate Denial-Of-Service Attacks on Retrieval-Augmented Generation of LLMs

Whitepaper called Hoist With His Own Petard: Inducing Guardrails To Facilitate Denial-Of-Service Attacks On Retrieval-Augmented Generation Of LLMs...

LASHED: LLMs and Static Hardware Analysis for Early Detection of RTL Bugs

While static analysis is useful in detecting early-stage hardware security bugs, its efficacy is limited because it requires information to form checks and is often unable to explain the security impact of a detected vulnerability. Large Language Models can be useful in filling these gaps by...

Graph Privacy: a Heterogeneous Federated GNN for Trans-Border Financial Data Circulation

The sharing of external data has become a strong demand of financial institutions, but the privacy issue has led to the difficulty of interconnecting different platforms and the low degree of data openness. To effectively solve the privacy problem of financial data in trans-border flow and sharin...

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding

Binary code analysis plays a pivotal role in the field of software security and is widely used in tasks such as software maintenance, malware detection, software vulnerability discovery, patch analysis, etc. However, unlike source code, reverse engineers face significant challenges in understandi...

XBreaking: Explainable Artificial Intelligence for Jailbreaking LLMs

Large Language Models are fundamental actors in the modern IT landscape dominated by AI solutions. However, security threats associated with them might prevent their reliable adoption in critical application scenarios such as government organizations and medical institutions. For this reason,...

Unlocking User-Oriented Pages: Intention-Driven Black-Box Scanner for Real-World Web Applications

Black-box scanners have played a significant role in detecting vulnerabilities for web applications. A key focus in current black-box scanning is increasing test coverage i.e., accessing more web pages. However, since many web applications are user-oriented, some deep pages can only be accessed...

Guard Against GenAI and LLM Risks from Development to Deployment with Qualys TotalAI

Artificial intelligence is fundamentally reshaping the enterprise. From automating customer service to accelerating code generation, large language models LLMs are rapidly becoming embedded in how businesses operate and compete. But as organizations embrace this innovation, they are also opening...

allennlp (>=0.9.1.dev20200415, <=1.0.0rc4.dev20200511), allennlp-models (>=1.0.0rc2, <=1.0.0rc3) +6 more potentially affected by CVE-2025-1194 via transformers (2.8.0)

transformers PYPI version 2.8.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - allennlp =0.9.1.dev20200415, =1.0.0rc2, =0.1.9, =0.0.2, =0.1.0, =0.3.2 Source cves: CVE-2025-1194 Source advisory: OSV:GHSA-FPWR-67PX-3Q...

adamix-gpt2 (>=0.0.1, <=0.0.2), allennlp (>=1.2.0rc1.dev20200930, <=1.2.0rc1.dev20201021) +6 more potentially affected by CVE-2025-1194 via transformers (3.3.1)

transformers PYPI version 3.3.1 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - adamix-gpt2 =0.0.1, =1.2.0rc1.dev20200930, =1.2.0rc1.dev20201007, =0.0.1, =0.5.0, =0.1.11, =0.1.12 - mc-transformers 0.1.11 Source cves:...

allennlp (>=1.0.0rc4.dev20200513, <=1.0.0rc6.dev20200601), allennlp-models (>=1.0.0rc4, <=1.0.0rc5) potentially affected by CVE-2025-1194 via transformers (2.9.1)

transformers PYPI version 2.9.1 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - allennlp =1.0.0rc4.dev20200513, =1.0.0rc4, =1.0.0rc5 Source cves: CVE-2025-1194 Source advisory: OSV:GHSA-FPWR-67PX-3QHX...

allennlp (>=1.2.0rc1.dev20201022, <=1.2.1), allennlp-models (>=1.2.0rc1.dev20201022, <=1.2.1) +5 more potentially affected by CVE-2025-1194 via transformers (3.4.0)

transformers PYPI version 3.4.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - allennlp =1.2.0rc1.dev20201022, =1.2.0rc1.dev20201022, =0.0.4, =0.0.1, =2.2.5, =2.3.1 Source cves: CVE-2025-1194 Source advisory:...

Robustness Via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction

Large language models LLMs have demonstrated impressive performance and have come to dominate the field of natural language processing NLP across various tasks. However, due to their strong instruction-following capabilities and inability to distinguish between instructions and data content, LLMs...

Token-Efficient Prompt Injection Attack: Provoking Cessation in LLM Reasoning Via Adaptive Token Compression

While reasoning large language models LLMs demonstrate remarkable performance across various tasks, they also contain notable security vulnerabilities. Recent research has uncovered a "thinking-stopped" vulnerability in DeepSeek-R1, where model-generated reasoning tokens can forcibly interrupt th...

Enhancing Leakage Attacks on Searchable Symmetric Encryption Using LLM-Based Synthetic Data Generation

Searchable Symmetric Encryption SSE enables efficient search capabilities over encrypted data, allowing users to maintain privacy while utilizing cloud storage. However, SSE schemes are vulnerable to leakage attacks that exploit access patterns, search frequency, and volume information. Existing...