CVE-2026-1237

Vulnerable cross-model authorization in juju. If a charm's cross-model permissions are revoked or expire, a malicious user who is able to update database records can mint an invalid macaroon that is incorrectly validated by the juju controller, enabling a charm to maintain otherwise revoked or...

CVE-2026-1237

Vulnerable cross-model authorization in juju. If a charm's cross-model permissions are revoked or expire, a malicious user who is able to update database records can mint an invalid macaroon that is incorrectly validated by the juju controller, enabling a charm to maintain otherwise revoked or...

CVE-2026-1237

Vulnerable cross-model authorization in juju. If a charm's cross-model permissions are revoked or expire, a malicious user who is able to update database records can mint an invalid macaroon that is incorrectly validated by the juju controller, enabling a charm to maintain otherwise revoked or...

CVE-2026-1237

Vulnerable cross-model authorization in juju. If a charm's cross-model permissions are revoked or expire, a malicious user who is able to update database records can mint an invalid macaroon that is incorrectly validated by the juju controller, enabling a charm to maintain otherwise revoked or...

CVE-2026-1237

Summary: CVE-2026-1237 describes a vulnerability in Juju where broken cross-model authorization allows a charm to retain access after permissions are revoked or expired by minting an invalid macaroon that the controller erroneously accepts. The root cause is that the Juju controller may fail to v...

Juju security vulnerabilities

Juju is a publicly available application orchestration engine developed by Canonical Juju. There is a security vulnerability in Juju, which stems from a flaw in cross-model authorization. This vulnerability could allow malicious users to maintain privileges that have been revoked or expired...

PT-2026-5129

Name of the Vulnerable Software and Affected Versions juju affected versions not specified Description A flaw exists in juju related to cross-model authorization. If permissions for a charm in a cross-model relation are revoked or expire, a malicious user capable of updating database records can...

Multi-Faceted Attack: Exposing Cross-Model Vulnerabilities in Defense-Equipped Vision-Language Models

The growing misuse of Vision-Language Models VLMs has led providers to deploy multiple safeguards, including alignment tuning, system prompts, and content moderation. However, the real-world robustness of these defenses against adversarial attacks remains underexplored. We introduce Multi-Faceted...

Jailbreak Mimicry: Automated Discovery of Narrative-Based Jailbreaks for Large Language Models

Large language models LLMs remain vulnerable to sophisticated prompt engineering attacks that exploit contextual framing to bypass safety mechanisms, posing significant risks in cybersecurity applications. We introduce Jailbreak Mimicry, a systematic methodology for training compact attacker mode...

You can poison AI with just 250 dodgy documents

Researchers have shown how you can corrupt an AI and make it talk gibberish by tampering with just 250 documents. The attack, which involves poisoning the data that an AI trains on, is the latest in a long line of research that has uncovered vulnerabilities in AI models. Anthropic which produces...

Mind the Gap: Evaluating Model- and Agentic-Level Vulnerabilities in LLMs with Action Graphs

As large language models transition to agentic systems, current safety evaluation frameworks face critical gaps in assessing deployment-specific risks. We introduce AgentSeer, an observability-based evaluation framework that decomposes agentic executions into granular action and component graphs,...

DAVSP: Safety Alignment for Large Vision-Language Models Via Deep Aligned Visual Safety Prompt

Large Vision-Language Models LVLMs have achieved impressive progress across various applications but remain vulnerable to malicious queries that exploit the visual modality. Existing alignment approaches typically fail to resist malicious queries while preserving utility on benign ones effectivel...

Efficient and Stealthy Jailbreak Attacks Via Adversarial Prompt Distillation from LLMs to SLMs

Attacks on large language models LLMs in jailbreaking scenarios raise many security and ethical issues. Current jailbreak attack methods face problems such as low efficiency, high computational cost, and poor cross-model adaptability and versatility, which make it difficult to cope with the rapid...

FFCBA: Feature-Based Full-Target Clean-Label Backdoor Attacks

Backdoor attacks pose a significant threat to deep neural networks, as backdoored models would misclassify poisoned samples with specific triggers into target classes while maintaining normal performance on clean samples. Among these, multi-target backdoor attacks can simultaneously target multip...

CVE-2024-7557 Odh-dashboard: odh-model-controller: cross-model authentication bypass in openshift ai

A vulnerability was found in OpenShift AI that allows for authentication bypass and privilege escalation across models within the same namespace. When deploying AI models, the UI provides the option to protect models with authentication. However, credentials from one model can be used to access...

Red Hat OpenShift Assisted Installer 访问控制错误漏洞

Red Hat OpenShift Assisted Installer is an assisted boot installer from Red Hat USA. An access control error vulnerability exists in Red Hat OpenShift Assisted Installer that stems from the presence of a cross-model authentication bypass vulnerability...