CVE-2026-55574

CVE-2026-55574 affects vLLM prior to 0.24.0, where structured_outputs.regex passes an unguarded user-supplied regex to grammar backends (xgrammar and outlines). In xgrammar, the string reaches the regex compiler without a timeout guard; in outlines, validation overlooks regex complexity (e.g., ne...

CVE-2026-44934

A information disclosure when DEBUG loglevel is set in SUSE Rancher AI Agent 1.0 before 1.0.2 could leak API keys or LLM response text with potential sensitive data into logfiles, allowing local attackers to misuse respective gained data or credentials...

CVE-2026-44934

A information disclosure when DEBUG loglevel is set in SUSE Rancher AI Agent 1.0 before 1.0.2 could leak API keys or LLM response text with potential sensitive data into logfiles, allowing local attackers to misuse respective gained data or credentials...

CVE-2026-58446

Presenton before 0.8.8-beta bundles an MCP server that, on server/Docker deployments configured with session authentication AUTHUSERNAME/AUTHPASSWORD, is reachable unauthenticated at /mcp because the nginx front-end does not apply the authrequest gate to that path and the MCP server auto-mints a...

282 iOS AI Apps Leak API Keys and Open AI Proxy Access in Network Traffic Study

Researchers tested 444 AI chatbot apps for iPhone and found that 282 of them, nearly two-thirds, exposed paid AI access through their network traffic. In many cases, the path in was visible just by watching what the app sent: a plaintext API key, a reusable token, or a backend server that accepte...

PYSEC-2026-478 PraisonAI's unauthenticated A2A official example can reach real LLM-driven `eval()` tool execution

Summary The first-party PraisonAI A2A server example combines three behaviors into a remotely exploitable Critical chain: 1. The example exposes an A2A server without configuring authtoken. 2. The same example binds the server to 0.0.0.0. 3. The example registers a calculateexpression tool...

PYSEC-2026-461 PraisonAI Vulnerable to OS Command Injection

The executecommand function and workflow shell execution are exposed to user-controlled input via agent workflows, YAML definitions, and LLM-generated tool calls, allowing attackers to inject arbitrary shell commands through shell metacharacters. --- Description PraisonAI's workflow system and...

CVE-2026-54235

A flaw was found in vLLM, an inference and serving engine for large language models LLMs. The temperature validation gates, which use comparison operators, incorrectly handle Not-a-Number NaN and positive Infinity values in Python's IEEE 754 float semantics. These invalid values can bypass...

CVE-2026-45792

rtk filters and compresses command outputs before they reach your LLM context. Prior to 0.32.0, RTK Rust Token Killer improperly trusts project-local configuration files. RTK automatically loads .rtk/filters.toml from the working directory with highest priority and without user notification. An...

CVE-2026-45792 RTK improperly trusts project-local filter configuration, allowing silent tampering of command output shown to LLM

rtk filters and compresses command outputs before they reach your LLM context. Prior to 0.32.0, RTK Rust Token Killer improperly trusts project-local configuration files. RTK automatically loads .rtk/filters.toml from the working directory with highest priority and without user notification. An...

PT-2026-51583

Name of the Vulnerable Software and Affected Versions rtk versions prior to 0.42.2 Description A flaw in the permission splitter logic fails to conservatively split or reject certain Bash shell constructs that create command-execution boundaries or nested execution. This improper input validation...

CVE-2026-54235

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.23.1rc0, ll temperature validation gates use comparison operators , which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagat...

CVE-2026-47155

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.22.0, vLLM's revision pinning controls do not consistently apply to all artifacts loaded for a model. A deployment that supplies --revision or --code-revision can still load dynamic code, GGUF files, image...

CVE-2026-54233

Affected software: vLLM (inference/serving engine). Vulnerability: decoding an audio file on the /v1/audio/transcriptions endpoint can cause extreme memory growth. A 25 MB OPUS upload decodes to about 14.9 GB of float32 PCM, because the audio decoder concatenates all frames in memory before retur...

CVE-2026-49468 LiteLLM: Authentication Bypass via Host Header Injection

LiteLLM is a proxy server AI Gateway to call LLM APIs in OpenAI or native format. Prior to 1.84.0, This vulnerability is fixed in 1.84.0...

EUVD-2026-38332

LangChain is a framework for building agents and LLM-powered applications. Prior to 1.3.9, several LangChain components that resolve filesystem paths or expand search patterns do not consistently confine the resolved path to the intended root directory. Affected behaviors include: a file-search...

CVE-2026-53538 vulnerabilities

Vulnerabilities for packages: wazuh-manager, wazuh-manager-fips, litellm, airflow, tritonserver-backend-vllm-cuda-12.9, airflow-postgres-fips, airflow-core...

CVE-2026-46517

LMDeploy is a toolkit for compressing, deploying, and serving large language models. In versions 0.12.3 and prior, hardcoded "trustremotecode=True" enables HF supply-chain RCE without user opt-in. At time of publication, there are no publicly available patches...

The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems

Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross. Within this broader red-line scenario, autonomous penetration represents a core enabling capability and...

Mind Your Key: An Empirical Study of LLM API Credential Leakage in IOS Apps

The rapid integration of large language models LLMs into mobile applications has introduced a new class of credential security risk: leaked credentials that grant unauthorized access to LLM inference services, causing financial damage to developers. Prior work on credential leakage has focused...