31 matches found
osint-pattern-analyzer
OSINT Pattern Analyzer Tool for automating OSINT investigations across multiple identity vectors phone numbers, usernames and future email support, designed to extract structured data and enable pattern detection and correlation analysis. Features Phone OSINT Search phone numbers across multiple...
OST-C2-Spec
Draft: Request for Discussion Abstract This document provides an overview of Version 1 of the OST C&C Specification. It is intended to provide a detailed description of messages and the fields within those messages. Introduction The motivation behind this specification is to provide a C&C messagi...
standalone_tdo
TDO Standalone Extractor A powerful, standalone command-line tool for extracting Cyber Threat Intelligence CTI from documents using Large Language Models with advanced structured output capabilities. 🚀 Features Multi-format Document Support : PDF, DOCX, TXT, and Markdown files Advanced LLM...
CVE-2026-105757
A flaw was found in vLLM. A remote authenticated attacker can cause a Denial of Service DoS by submitting specially crafted structured-output requests. Due to improper exception handling around structured-output processing, certain request-level errors fail to be contained and propagate directly...
EUVD-2026-92847
vLLM: Structured-output request errors escape the request boundary and terminate the shared EngineCore — engine-fatal denial of service 3 sites...
vLLM: Structured-output request errors escape the request boundary and terminate the shared EngineCore — engine-fatal denial of service (3 sites)
Affected - Ecosystem / package: pip / vllm - Affected versions: vLLM ≤ 0.25.1 confirmed on 0.25.1, commit 752a3a504485. The lower bound predates 0.25.1; maintainers can confirm how far back each path reaches. Summary Three structured-output request paths let an ordinary request reach a condition...
GHSA-85XF-C7HM-WHQW vLLM: Structured-output request errors escape the request boundary and terminate the shared EngineCore — engine-fatal denial of service (3 sites)
Affected - Ecosystem / package: pip / vllm - Affected versions: vLLM ≤ 0.25.1 confirmed on 0.25.1, commit 752a3a504485. The lower bound predates 0.25.1; maintainers can confirm how far back each path reaches. Summary Three structured-output request paths let an ordinary request reach a condition...
PYSEC-2026-4199
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. A per-request backend mismatch can re-raise a grammar compilation exception, padding produced by t...
CVE-2026-105757
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. A per-request backend mismatch can re-raise a grammar compilation exception, padding produced by t...
CVE-2026-105757
The vLLM inference and serving engine for large language models is vulnerable to a denial of service (DoS) condition in versions prior to 0.30.0 . The root cause is that structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. Specific tri...
CVE-2026-105757 vLLM: Structured-output request errors escape the request boundary and terminate the shared EngineCore — engine-fatal denial of service (3 sites)
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. A per-request backend mismatch can re-raise a grammar compilation exception, padding produced by t...
CVE-2026-105757 vLLM: Structured-output request errors escape the request boundary and terminate the shared EngineCore — engine-fatal denial of service (3 sites)
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. A per-request backend mismatch can re-raise a grammar compilation exception, padding produced by t...
CVE-2026-105757 vLLM: Structured-output request errors escape the request boundary and terminate the shared EngineCore — engine-fatal denial of service (3 sites)
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. A per-request backend mismatch can re-raise a grammar compilation exception, padding produced by t...
EUVD-2026-58063
vLLM: ReDoS via structuredoutputs.regex in the lm-format-enforcer backend no compile timeout — missed sibling of GHSA-rwxx-mrjm-wc2m...
Allocation of Resources Without Limits or Throttling
Overview vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling via the structuredoutputs.regex parameter in the lm-format-enforcer backend. An attacker can...
CVE-2026-73556
vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the structuredoutputs.regex parameter in vllm/v1/structuredoutput/backendlmformatenforcer.py is passed to lmformatenforcer.RegexParser without compileregexwithtimeout or validation in...
CVE-2026-73556
vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the structuredoutputs.regex parameter in vllm/v1/structuredoutput/backendlmformatenforcer.py is passed to lmformatenforcer.RegexParser without compileregexwithtimeout or validation in...
CVE-2026-73556 vLLM: ReDoS via structured_outputs.regex in the lm-format-enforcer backend (no compile timeout) — missed sibling of CVE-2026-55574
vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the structuredoutputs.regex parameter in vllm/v1/structuredoutput/backendlmformatenforcer.py is passed to lmformatenforcer.RegexParser without compileregexwithtimeout or validation in...
Self-Correcting Structured Output in Spring AI 2.0
Large language models are text-in, text-out systems — their interface is natural language. Natural language is a great interface for humans and a poor one for software. The moment downstream code needs to route on a field, persist a value, or branch on a result, the conversation has to become a...
