LLMtary

LLMtary Elementary — AI-Powered Penetration Testing Platform...

VulGD: A LLM-Powered Dynamic Open-Access Vulnerability Graph Database

Software vulnerabilities continue to pose significant threats to modern information systems, requiring a timely and accurate risk assessment. Public repositories, such as the National Vulnerability Database and CVE details, are regularly updated, but predominantly utilize relational data models...

SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training

The field of cybersecurity is confronted with two interrelated challenges: a worldwide deficit of qualified practitioners and ongoing human-factor weaknesses that account for the bulk of security incidents. To tackle these issues, we present SentinelSphere, a platform driven by artificial...

PYSEC-2026-144

vLLM is an inference and serving engine for large language models LLMs. From 0.7.0 to before 0.19.0, the VideoMediaIO.loadbase64 method at vllm/multimodal/media/video.py splits video/jpeg data URLs by comma to extract individual JPEG frames, but does not enforce a frame count limit. The numframes...

CVE-2026-34755

vLLM's VideoMediaIO.load_base64("video/jpeg") path has an unbounded frame-splitting bug: data.split(",") bypasses the intended frame-count limit (default 32) used by the binary path, allowing a single request with thousands of comma-separated base64 JPEG frames. This can cause the server to decod...

SmartContract-VulnHunter

🛡️ SmartContract VulnHunter The ultimate smart contract securi...

vLLM 安全漏洞

vLLM is an open-source LLM-based inference and service engine that features high throughput and efficient memory usage. Versions of vLLM prior to 0.1.0 to 0.19.0 contained security vulnerabilities. These vulnerabilities stemmed from the lack of upper limit validation for the n parameter in the...

Automating Cloud Security and Forensics through a Secure-By-Design Generative AI Framework

As cloud environments become increasingly complex, cybersecurity and forensic investigations must evolve to meet emerging threats. Large Language Models LLMs have shown promise in automating log analysis and reasoning tasks, yet they remain vulnerable to prompt injection attacks and lack forensic...

PYSEC-2026-2299

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing tomono, while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results...

AgentWatcher: A Rule-Based Prompt Injection Monitor

Large language models LLMs and their applications, such as agents, are highly vulnerable to prompt injection attacks. State-of-the-art prompt injection detection methods have the following limitations: 1 their effectiveness degrades significantly as context length increases, and 2 they lack...

vLLM 输入验证错误漏洞

vLLM is an open-source LLM-based inference and service engine that features high throughput and efficient memory usage. Versions of vLLM prior to 0.5.5 and 0.18.0 contained a vulnerability related to input validation errors. This vulnerability stemmed from inconsistencies in the audio mono downmi...

UBUNTU-CVE-2026-34159

llama.cpp is an inference of several LLM models in C/C++. Prior to version b8492, the RPC backend's deserializetensor skips all bounds validation when a tensor's buffer field is 0. An unauthenticated attacker can read and write arbitrary process memory via crafted GRAPHCOMPUTE messages. Combined...

Automated Framework to Evaluate and Harden LLM System Instructions against Encoding Attacks

System Instructions in Large Language Models LLMs are commonly used to enforce safety policies, define agent behavior, and protect sensitive operational context in agentic AI applications. These instructions may contain sensitive information such as API credentials, internal policies, and...

The vulnerability of LLM-powered system startups, related to deficiencies in authentication mechanisms, allows attackers to view and modify settings.

The vulnerability of LLM-based system startups is related to deficiencies in authentication mechanisms. Exploiting this vulnerability allows a remote attacker to view and modify system settings...

Awesome LLM Apps 安全漏洞

Awesome LLM Apps is a collection of large language model applications personally developed by Shubham Saboo. Awesome LLM Apps contains security vulnerabilities, which stem from improper isolation of session-specific environment variables, potentially leading to cross-session information leaks...

Safeguarding LLMs against Misuse and AI-Driven Malware Using Steganographic Canaries

AI-powered malware increasingly exploits cloud-hosted generative-AI services and large language models LLMs as analysis engines for reconnaissance and code generation. Simultaneously, enterprise uploads expose sensitive documents to third-party AI vendors. Both threats converge at the AI service...

CVE-2026-27893

vLLM is an inference and serving engine for large language models LLMs. Starting in version 0.10.1 and prior to version 0.18.0, two model implementation files hardcode trustremotecode=True when loading sub-components, bypassing the user's explicit --trust-remote-code=False security opt-out. This...

Unveiling the Resilience of LLM-Enhanced Search Engines against Black-Hat SEO Manipulation

The emergence of Large Language Model-enhanced Search Engines LLMSEs has revolutionized information retrieval by integrating web-scale search capabilities with AI-powered summarization. While these systems demonstrate improved efficiency over traditional search engines, their security implication...

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...

Agent Audit: A Security Analysis System for LLM Agent Applications

What should a developer inspect before deploying an LLM agent: the model, the tool code, the deployment configuration, or all three? In practice, many security failures in agent systems arise not from model weights alone, but from the surrounding software stack: tool functions that pass untrusted...