RAGAS has an Arbitrary File Read vulnerability

An Arbitrary File Read vulnerability exists in the ImageTextPromptValue class in Exploding Gradients RAGAS v0.2.3 to v0.2.14. The vulnerability stems from improper validation and sanitization of URLs supplied in the retrievedcontexts parameter when handling multimodal inputs...

EUVD-2025-208315

An Arbitrary File Read vulnerability exists in the ImageTextPromptValue class in Exploding Gradients RAGAS v0.2.3 to v0.2.14. The vulnerability stems from improper validation and sanitization of URLs supplied in the retrievedcontexts parameter when handling multimodal inputs...

CVE-2025-45691

An Arbitrary File Read vulnerability exists in the ImageTextPromptValue class in Exploding Gradients RAGAS v0.2.3 to v0.2.14. The vulnerability stems from improper validation and sanitization of URLs supplied in the retrievedcontexts parameter when handling multimodal inputs...

CVE-2025-45691

CVE-2025-45691 affects VibrantLabs RAGAS (up to v0.4.3); the vulnerability lies in improper validation of URLs in retrieved_contexts during multimodal input processing, enabling Server-Side Request Forgery (SSRF) and arbitrary file reads. Several connected sources describe exploitation via manipu...

CVE-2025-45691

An Arbitrary File Read vulnerability exists in the ImageTextPromptValue class in Exploding Gradients RAGAS v0.2.3 to v0.2.14. The vulnerability stems from improper validation and sanitization of URLs supplied in the retrievedcontexts parameter when handling multimodal inputs...

CVE-2025-45691

An Arbitrary File Read vulnerability exists in the ImageTextPromptValue class in Exploding Gradients RAGAS v0.2.3 to v0.2.14. The vulnerability stems from improper validation and sanitization of URLs supplied in the retrievedcontexts parameter when handling multimodal inputs...

PT-2026-23468

Name of the Vulnerable Software and Affected Versions Exploding Gradients RAGAS versions 0.2.3 through 0.2.14 Description An arbitrary file read issue exists in the ImageTextPromptValue class. This is due to insufficient validation and sanitization of URLs provided in the retrieved contexts...

CVE-2025-45691

An Arbitrary File Read vulnerability exists in the ImageTextPromptValue class in Exploding Gradients RAGAS v0.2.3 to v0.2.14. The vulnerability stems from improper validation and sanitization of URLs supplied in the retrievedcontexts parameter when handling multimodal inputs...

Self-Purification Mitigates Backdoors in Multimodal Diffusion Language Models

Multimodal Diffusion Language Models MDLMs have recently emerged as a competitive alternative to their autoregressive counterparts. Yet their vulnerability to backdoor attacks remains largely unexplored. In this work, we show that well-established data-poisoning pipelines can successfully implant...

Deep Learning for Contextualized NetFlow-Based Network Intrusion Detection: Methods, Data, Evaluation and Deployment

Network Intrusion Detection Systems NIDS have progressively shifted from signature-based techniques toward machine learning and, more recently, deep learning methods. Meanwhile, the widespread adoption of encryption has reduced payload visibility, weakening inspection pipelines that depend on...

CVE-2026-22778

A flaw was found in vLLM, an inference and serving engine for large language models LLMs. A remote attacker can exploit this vulnerability by sending a specially crafted video URL to vLLM's multimodal endpoint. This action causes vLLM to leak a heap memory address, significantly reducing the...

CVE-2026-22778

vLLM is an inference and serving engine for large language models LLMs. From 0.8.3 to before 0.14.1, when an invalid image is sent to vLLM's multimodal endpoint, PIL throws an error. vLLM returns this error to the client, leaking a heap address. With this leak, we reduce ASLR from 4 billion guess...

CVE-2026-22778

Summary of CVE-2026-22778 : A vulnerability in vLLM (0.8.3–0.14.0) lets an attacker send an invalid image to the multimodal endpoint, causing PIL to leak a heap address. This information disclosure can be chained with a heap overflow in the JPEG2000 decoder used by OpenCV/FFmpeg to achieve remote...

CVE-2026-22778

vLLM is an inference and serving engine for large language models LLMs. From 0.8.3 to before 0.14.1, when an invalid image is sent to vLLM's multimodal endpoint, PIL throws an error. vLLM returns this error to the client, leaking a heap address. With this leak, we reduce ASLR from 4 billion guess...

CVE-2026-22778 vLLM leaks a heap address when PIL throws an error

vLLM is an inference and serving engine for large language models LLMs. From 0.8.3 to before 0.14.1, when an invalid image is sent to vLLM's multimodal endpoint, PIL throws an error. vLLM returns this error to the client, leaking a heap address. With this leak, we reduce ASLR from 4 billion guess...

CVE-2026-22778 vLLM leaks a heap address when PIL throws an error

vLLM is an inference and serving engine for large language models LLMs. From 0.8.3 to before 0.14.1, when an invalid image is sent to vLLM's multimodal endpoint, PIL throws an error. vLLM returns this error to the client, leaking a heap address. With this leak, we reduce ASLR from 4 billion guess...

PT-2026-5710

Name of the Vulnerable Software and Affected Versions vLLM versions 0.8.3 through 0.14.0 Description vLLM is an inference and serving engine for large language models LLMs. A chain of issues allows for remote code execution RCE when a video model is enabled. First, sending an invalid image to the...

EUVD-2026-4711

vLLM vulnerable to Server-Side Request Forgery SSRF through MediaConnector...

CVE-2026-22773

A flaw was found in vLLM, an inference and serving engine for large language models LLMs. A remote attacker can exploit this vulnerability by sending a specially crafted 1x1 pixel image to a vLLM engine serving multimodal models that use the Idefics3 vision model implementation. This leads to a...

FOCA: Multimodal Malware Classification Via Hyperbolic Cross-Attention

In this work, we introduce FOCA, a novel multimodal framework for malware classification that jointly leverages audio and visual modalities. Unlike conventional Euclidean-based fusion methods, FOCA is the first to exploit the intrinsic hierarchical relationships between audio and visual...