unstructured: Server-Side Request Forgery in the URL-based partitioning

vLLM is an inference and serving engine for large language models (LLMs). The SSRF protection fix for CVE-2026-24779 add in 0.15.1 can be bypassed in the load_from

Vibe-Trading LLM-callable tools permit command execution, code injection, and SSRF

model inference. In versions 1.4.0 until 1.4.19, the file upload processing system contains an SSRF vulnerability that allows unauthenticated remote attackers to force the server to make arbitrary HTTP requests

Python package designed for quick prototyping. This vulnerability relates to **Server-Side Request Forgery (SSRF)** in the `/queue/join` endpoint. Gradio’s `async_save_url_to_cache` function allows attackers

server-side request forgery (SSRF) vulnerability was identified in the notebook viewer of GitHub Enterprise Server. The notebook viewer validated the scheme and host of a user-supplied

components containing socket or urllib imports. This enables: (1) AWS credential theft via IMDSv1 SSRF with full IAM role permissions, (2) arbitrary file exfiltration from the container filesystem

injection. Attackers can exploit unblocked pandas functions like pd.read_json() to exfiltrate datasets, perform SSRF against internal services, or achieve code execution through the unauthenticated prediction

Flyto2 Core: Arbitrary file write via image.download (and other file

attackers can retrieve stored secrets like OpenAI API keys via GET /api/v1/config/ or trigger SSRF attacks by setting ollama_base_url to internal addresses like cloud IMDS via PUT /api/v1/config/mem0/llm

PraisonAI: Jobs API exposes agent-execution endpoints with no authentication

Feishu extension that allow attackers to fetch attacker-controlled remote URLs without SSRF protections via sendMediaFeishu function and markdown image processing. Attackers can influence tool calls through direct manipulation

Server-Side Request Forgery (SSRF) vulnerability exists in the RequestsToolkit component of the langchain-community package (specifically, langchain_community.agent_toolkits.openapi.toolkit.RequestsToolkit) in langchain-ai/langchain version 0.0.27. This vulnerability occurs because the toolkit

A Security Analysis of Verifiable Credential Verification Pipelines: Attack Surface Arising from JSON-LD/RDFC Canonicalization Compared with SD-JWT VC and mdoc

service (DoS) via poison graphs, context injection through @context, and server-side request forgery (SSRF) reachable via malicious URLs, all at default settings. A poison graph, whose blank-node references

OllamaDrama: Designing and Deploying a Honeypot to Measure Attacks on Exposed LLM Infrastructure

both the infrastructure and LLM layers. These included model management abuse, path traversal and SSRF probes, RCE and cryptocurrency mining payloads, resource exhaustion attempts, prompt injection, information extraction, and agent

PraisonAI: Missing Authentication for Critical Function and Improper Neutralization of

Penetration Testing of Agentic AI: A Comparative Security Analysis Across Models and Frameworks

system and 13 distinct attack scenarios that span prompt injection, Server Side Request Forgery (SSRF), SQL injection, and tool misuse. Our 130 total test cases reveal significant security disparities: AutoGen