Towards the Development of an LLM-Based Methodology for Automated Security Profiling in Compliance with Ukrainian Cybersecurity Regulations

In recent years, the pace of development of information technology in various areas has increased drastically, forcing cybersecurity specialists to constantly review existing processes in order to prevent unauthorized access to confidential information. Using Ukraine as a primary case study, this...

PYSEC-2026-2298

vLLM is an inference and serving engine for large language models LLMs. From 0.1.0 to before 0.19.0, a Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the n parameter in the ChatCompletionRequest and CompletionReques...

EUVD-2026-19351

vLLM is an inference and serving engine for large language models LLMs. From 0.1.0 to before 0.19.0, a Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the n parameter in the ChatCompletionRequest and CompletionReques...

CoopGuard: Stateful Cooperative Agents Safeguarding LLMs against Evolving Multi-Round Attacks

As Large Language Models LLMs are increasingly deployed in complex applications, their vulnerability to adversarial attacks raises urgent safety concerns, especially those evolving over multi-round interactions. Existing defenses are largely reactive and struggle to adapt as adversaries refine...

LLM-Enabled Open-Source Systems in the Wild: An Empirical Study of Vulnerabilities in GitHub Security Advisories

Large language models LLMs are increasingly embedded in open-source software OSS ecosystems, creating complex interactions among natural language prompts, probabilistic model outputs, and execution-capable components. However, it remains unclear whether traditional vulnerability disclosure...

Perceptual Gaps: ASCII Art and Overlapping Audio As CAPTCHA

As multimodal large language models LLMs advance, traditional CAPTCHAs have become obsolete at distinguishing humans from bots. To address this shift, this paper aims to investigate the possibility of using tasks for which humans have evolved highly specialised neural processing. We introduce two...

Automated Malware Family Classification Using Weighted Hierarchical Ensembles of Large Language Models

Malware family classification remains a challenging task in automated malware analysis, particularly in real-world settings characterized by obfuscation, packing, and rapidly evolving threats. Existing machine learning and deep learning approaches typically depend on labeled datasets, handcrafted...

Combating Data Laundering in LLM Training

Data rights owners can detect unauthorized data use in large language model LLM training by querying with proprietary samples. Often, superior performance e.g., higher confidence or lower loss on a sample relative to the untrained data implies it was part of the training corpus, as LLMs tend to...

PT-2026-29877

Name of the Vulnerable Software and Affected Versions vLLM versions 0.5.5 through 0.17.999 Description vLLM, an inference and serving engine for large language models LLMs, exhibits an inconsistency in audio processing. Versions 0.5.5 through 0.17.999 utilize numpy.mean for mono downmixing via...

Hidden Ads: Behavior Triggered Semantic Backdoors for Advertisement Injection in Vision Language Models

Vision-Language Models VLMs are increasingly deployed in consumer applications where users seek recommendations about products, dining, and services. We introduce Hidden Ads, a new class of backdoor attacks that exploit this recommendation-seeking behavior to inject unauthorized advertisements...

CVE-2026-27893

CVE-2026-27893 affects vLLM’s inference/serving engine. From version 0.10.1 up to (but not including) 0.18.0, two model implementation files hardcode trust_remote_code=True when loading sub-components, bypassing the user’s --trust-remote-code=False security opt-out. This enables remote code execu...

Shape and Substance: Dual-Layer Side-Channel Attacks on Local Vision-Language Models

On-device Vision-Language Models VLMs promise data privacy via local execution. However, we show that the architectural shift toward Dynamic High-Resolution preprocessing e.g., AnyRes introduces an inherent algorithmic side-channel. Unlike static models, dynamic preprocessing decomposes images in...

TreeTeaming: Autonomous Red-Teaming of Vision-Language Models Via Hierarchical Strategy Exploration

The rapid advancement of Vision-Language Models VLMs has brought their safety vulnerabilities into sharp focus. However, existing red teaming methods are fundamentally constrained by an inherent linear exploration paradigm, confining them to optimizing within a predefined strategy set and...

Leveraging Large Language Models for Trustworthiness Assessment of Web Applications

The widespread adoption of web applications has made their security a critical concern and has increased the need for systematic ways to assess whether they can be considered trustworthy. However, "trust" assessment remains an open problem as existing techniques primarily focus on detecting known...

Towards Leveraging LLMs to Generate Abstract Penetration Test Cases from Software Architecture

Software architecture models capture early design decisions that strongly influence system quality attributes, including security. However, architecture-level security assessment and feedback are often absent in practice, allowing security weaknesses to propagate into later phases of the software...

CVE-2026-32114

Discourse (open‑source discussion platform) contains an Insecure Direct Object Reference (IDOR) vulnerability. Prior to versions 2026.3.0-latest.1, 2026.2.1, and 2026.1.2, any authenticated user can access metadata about AI personas, features, and LLM models by supplying their identifiers. This m...

CVE-2026-32114 Discourse's unscoped status lookups leak restricted metadata

Discourse is an open-source discussion platform. Prior to versions 2026.3.0-latest.1, 2026.2.1, and 2026.1.2, there is an Insecure Direct Object Reference IDOR vulnerability that allows any authenticated user to access metadata about AI personas, features, and LLM models by providing their...

The vulnerability of the PyNcclPipe class in the library for working with Large Language Models (LLMs) like vLLM allows a hacker to execute arbitrary code.

The vulnerability of the PyNcclPipe class in the library for working with Large Language Models LLMs like vLLM is related to deficiencies in the deserialization mechanism. Exploiting this vulnerability allows a remote attacker to execute arbitrary code...

CVE-2026-27068

Improper Neutralization of Input During Web Page Generation 'Cross-site Scripting' vulnerability in Ryan Howard Website LLMs.txt website-llms-txt allows Reflected XSS.This issue affects Website LLMs.txt: from n/a through = 8.2.6...

Measuring and Exploiting Confirmation Bias in LLM-Assisted Security Code Review

Security code reviews increasingly rely on systems integrating Large Language Models LLMs, ranging from interactive assistants to autonomous agents in CI/CD pipelines. We study whether confirmation bias i.e., the tendency to favor interpretations that align with prior expectations affects LLM-bas...