PYSEC-2026-4192

Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.27.0 until 2.131.0, Docling plugin factories in docling/models/factories/basefactory.py call loadsetuptoolsentrypoints before applying the allowexternalplugins...

CVE-2026-105746 Docling: KServe v2 OCR engine does not enforce enable_remote_services

Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.83.0 until 2.131.0, the KServeV2OcrModel class defined in docling/models/stages/ocr/kservev2ocrmodel.py sends page images to its configured endpoint without checki...

CVE-2026-105745 Docling: Plugin entry points are imported before the allow_external_plugins check

Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.27.0 until 2.131.0, Docling plugin factories in docling/models/factories/basefactory.py call loadsetuptoolsentrypoints before applying the allowexternalplugins...

CVE-2026-105745 Docling: Plugin entry points are imported before the allow_external_plugins check

Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.27.0 until 2.131.0, Docling plugin factories in docling/models/factories/basefactory.py call loadsetuptoolsentrypoints before applying the allowexternalplugins...

CVE-2026-105745 Docling: Plugin entry points are imported before the allow_external_plugins check

Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.27.0 until 2.131.0, Docling plugin factories in docling/models/factories/basefactory.py call loadsetuptoolsentrypoints before applying the allowexternalplugins...

CVE-2026-105745

The Docling library (versions 2.27.0 through 2.131.0 ) is vulnerable to arbitrary code execution due to an incorrect behavior order in the docling/models/factories/base_factory.py component. The software calls load_setuptools_entrypoints() before validating the allow_external_plugins setting, cau...

NVIDIA Triton Inference Server Auth Bypass (CVE-2026-24207)

Exploits CVE-2026-24207 in NVIDIA Triton Inference Server = 26.02: unauthenticated access to restricted HTTP APIs including /models and model management endpoints. !/usr/bin/env python3 -- coding: utf-8 -- """NVIDIA Triton Inference Server auth bypass CVE-2026-24207.""" import json from kittysplo...

An Evaluation of the Semantic Understanding Capabilities of Large Language Models for Web Attack Payloads

Computer vision services delivered through Web interfaces and APIs process textual requests for image-resource acquisition, inference-task configuration, and result management, making Web attack-payload analysis relevant to their deployment security. Large language models LLMs can identify payloa...

Backdooring Sparse Autoencoders

Sparse autoencoders SAEs are increasingly used not only to interpret language models but also to intervene on their internal representations. We show that this creates a supply-chain attack surface: a maliciously modified SAE can induce attacker-chosen behavior when inserted into the forward pass...

CVE-2026-105745: Incorrect Behavior Order

Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.27.0 until 2.131.0, Docling plugin factories in docling/models/factories/basefactory.py call loadsetuptoolsentrypoints before applying the allowexternalplugins...

PT-2026-106248

Name of the Vulnerable Software and Affected Versions Docling versions 2.27.0 through 2.130.0 Description Plugin factories in docling/models/factories/base factory.py call the load setuptools entrypoints function before the allow external plugins setting is applied. This sequence allows every...

SAFESHIELD: A Decision-Organization Framework for Deployment-Time Safety of Small Language Models

Deployment-time safety of language models is commonly implemented through runtime guardrails such as input moderation, routing, retrieval verification, and output filtering. Existing deployment frameworks provide increasingly capable mechanisms for these functions, but offer limited guidance on h...

PT-2026-106280

Name of the Vulnerable Software and Affected Versions Joplin Server versions prior to 3.7.2 Description In the packages/server/src/models/TokenModel.ts file, CSRF, account-confirmation, email-change, and password-reset tokens are stored without a designated purpose. Additionally, the...

Which Image Property Carries the Jailbreak? A Controlled Dissection of Image-To-Text Jailbreaks

Image-to-text jailbreaks place harmful intent in text, image content, or the relationship between them. We examine image-side factors across four published attack families on a 313-prompt StrongREJECT slice, using five multimodal models and an additional appendix evaluation of InternVL3.5-8B. The...

AutoDP-LLM: Automating Data Pre-Processing for Intrusion Detection Systems Using Large Language Models

The increasing complexity and scale of modern cyber-attacks demand intelligent and computationally efficient Intrusion Detection Systems IDS. However, designing effective data pre-processing pipelines traditionally involves substantial trial-and-error effort and repeated evaluation of alternative...

The Poisoned Conversation: Privacy-Leaking Watermarks in Unified Multimodal Models

Multimodal models are increasingly shifting toward unified architectures that understand and generate text, images, and other modalities within a shared conversational context. This design enables fluid interaction across modalities, but it also changes the privacy threat model: Information...

Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models

Large language models remain vulnerable to jailbreaks, and automated red teaming is the standard way to find jailbreaks in large language models at scale. Current methods either draw more samples at test time through search, rewriting, and tree expansion, or train a stronger attacker offline with...

Static Bootstrap Placement for Encrypted Language Model Decoding

Language models increasingly serve prompts that carry private data, and secure inference under homomorphic encryption lets a client outsource the computation without revealing the prompt. Existing secure inference systems run a forward pass without consuming a token under encryption, and generati...

Forecasting Cybersecurity Incidents Using Geopolitical Data and Large Language Models

Predicting security incidents is a profound task critical for informing proactive defensive measures and cyber-insurance policies. Prior work tackling this problem mainly utilized structured, manually defined features based on network measurements e.g., protocol misconfigurations. Still, despite...

SecJev: Bringing Security Expertise to System One Decision Models

Security workflows need models that turn complex observations and explicit policies into decisions. System One models introduced by Jev return typed predictions and probabilities; security specialization supplies the domain expertise behind those predictions. We introduce SecJev, to our knowledge...