MaxKB 安全漏洞

MaxKB is an open-source question-answering system based on large language models and RAG, developed by 1Panel-dev. Versions of MaxKB prior to 2.7.1 contained a security vulnerability. This vulnerability stemmed from the use of storage-oriented cross-site scripting in the application name or icon...

Towards Automated Pentesting with Large Language Models

Large Language Models LLMs are redefining offensive cybersecurity by allowing the generation of harmful machine code with minimal human intervention. While attackers take advantage of dark LLMs such as XXXGPT and WolfGPT to produce malicious code, ethical hackers can follow similar approaches to...

MaxKB 代码注入漏洞

MaxKB is an open-source question-answering system based on large language models and RAG, developed by 1Panel-dev. Versions of MaxKB 2.2.1 and earlier have a code injection vulnerability. This vulnerability stems from incorrect handling of parameters in the file...

EUVD-2026-19671

text-generation-webui is an open-source web interface for running Large Language Models. Prior to 4.3, he superbooga and superboogav2 RAG extensions fetch user-supplied URLs via requests.get with zero validation — no scheme check, no IP filtering, no hostname allowlist. An attacker can access clo...

Guiding Symbolic Execution with Static Analysis and LLMs for Vulnerability Discovery

Symbolic execution detects vulnerabilities with precision, but applying it to large codebases requires harnesses that set up symbolic state, model dependencies, and specify assertions. Writing these harnesses has traditionally been a manual process requiring expert knowledge, which significantly...

Stop Fixating on Prompts: Reasoning Hijacking and Constraint Tightening for Red-Teaming LLM Agents

With the widespread application of LLM-based agents across various domains, their complexity has introduced new security threats. Existing red-team methods mostly rely on modifying user prompts, which lack adaptability to new data and may impact the agent's performance. To address the challenge,...

Swiss-Bench 003: Evaluating LLM Reliability and Adversarial Security for Swiss Regulatory Contexts

The deployment of large language models LLMs in Swiss financial and regulatory contexts demands empirical evidence of both production reliability and adversarial security, dimensions not jointly operationalized in existing Swiss-focused evaluation frameworks. This paper introduces Swiss-Bench 003...

Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing

The rapid advancement of Large Language Models LLMs has created new opportunities for Automated Penetration Testing AutoPT, spawning numerous frameworks aimed at achieving end-to-end autonomous attacks. However, despite the proliferation of related studies, existing research generally lacks...

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...

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...

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...

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...

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...

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...

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...