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...

Quantum-Safe Code Auditing: LLM-Assisted Static Analysis and Quantum-Aware Risk Scoring for Post-Quantum Cryptography Migration

The impending arrival of cryptographically relevant quantum computers CRQCs threatens the security foundations of modern software: Shor's algorithm breaks RSA, ECDSA, ECDH, and Diffie-Hellman, while Grover's algorithm reduces the effective security of symmetric and hash-based schemes. Despite NIS...

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...

LLM System SQL Injection

The vulnerability of the getTableSchemaSql function in the large language model LLM running and management system is related to the lack of measures taken to protect the SQL query structure. Exploiting this vulnerability allows a malicious actor to execute arbitrary SQL commands remotely...

LLM System Unauthorized Access

The vulnerability of LLM-powered systems is related to configuration errors in CORS policies. Exploiting this vulnerability can allow an attacker to gain unauthorized access to protected information...

LLM-powered system authentication bypass

The vulnerability of LLM-based system implementations is related to deficiencies in authentication procedures. Exploiting this vulnerability can allow a malicious actor to gain access to read, modify, and delete data...

LLM-powered system authentication bypass

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...

LLM system Import Community Item Code Execution

The vulnerability of the ImportedPlugin.importCommunityItemFromUrl function in the LLM system is related to an incorrect restriction on the path name of the catalog. Exploiting this vulnerability could allow a malicious actor to execute arbitrary code...

LLM system Frontend XSS

The vulnerability in the frontend/src/utils/chat/markdown.js script of the LLM system’s startup and management mechanism is related to insufficient protection of the web page structure. Exploiting this vulnerability could allow a malicious actor to perform XSS attacks across different websites...

CVE-2026-29872

A cross-session information disclosure vulnerability exists in the awesome-llm-apps project in commit e46690f99c3f08be80a9877fab52acacf7ab8251 2026-01-19. The affected Streamlit-based GitHub MCP Agent stores user-supplied API tokens in process-wide environment variables using os.environ without...

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...

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...

CVE-2026-33654

nanobot is a personal AI assistant. Prior to version 0.1.6, an indirect prompt injection vulnerability exists in the email channel processing module nanobot/channels/email.py, allowing a remote, unauthenticated attacker to execute arbitrary LLM instructions and subsequently, system tools without...

Arbitrary Code Injection

Langflow is vulnerable to Arbitrary Code Injection. The vulnerability is due to the validation process dynamically executing LLM‑generated Python code via exec, where the validation routine runs the generated code and an attacker who can influence the model output can achieve arbitrary server‑sid...

CVE-2026-33873

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to version 1.9.0, the Agentic Assistant feature in Langflow executes LLM-generated Python code during its validation phase. Although this phase appears intended to validate generated component code, the...

CVE-2026-33654

nanobot is a personal AI assistant. Prior to version 0.1.6, an indirect prompt injection vulnerability exists in the email channel processing module nanobot/channels/email.py, allowing a remote, unauthenticated attacker to execute arbitrary LLM instructions and subsequently, system tools without...

CVE-2026-27893

A flaw was found in vLLM, an inference and serving engine for large language models LLMs. Two model implementation files hardcode trustremotecode=True when loading sub-components. This bypasses the user's explicit --trust-remote-code=False security opt-out, allowing a remote attacker to achieve...

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...

CVE-2026-28788

Open WebUI is a self-hosted artificial intelligence platform designed to operate entirely offline. Prior to version 0.8.6, any authenticated user can overwrite any file's content by ID through the POST /api/v1/retrieval/process/files/batch endpoint. The endpoint performs no ownership check, so a...

Clawed and Dangerous: Can We Trust Open Agentic Systems?

Open agentic systems combine LLM-based planning with external capabilities, persistent memory, and privileged execution. They are used in coding assistants, browser copilots, and enterprise automation. OpenClaw is a visible instance of this broader class. Without much attention yet, their securit...