CVE-2026-55977 Bypass of application rate-limiting mechanism

Successful exploitation of this vulnerability could allow an attacker with local network access to bypass the application's rate-limiting mechanism, enabling brute-forcing of the screen-sharing code and potentially displaying harmful content on the affected screen...

CVE-2026-55977

Successful exploitation of this vulnerability could allow an attacker with local network access to bypass the application's rate-limiting mechanism, enabling brute-forcing of the screen-sharing code and potentially displaying harmful content on the affected screen...

CVE-2026-55977

Technical details about CVE-2026-55977 are not publicly available in the provided documents; monitor for updates from NVD and CVE sources.

CVE-2026-55977 Bypass of application rate-limiting mechanism

Successful exploitation of this vulnerability could allow an attacker with local network access to bypass the application's rate-limiting mechanism, enabling brute-forcing of the screen-sharing code and potentially displaying harmful content on the affected screen...

EUVD-2026-49714

Successful exploitation of this vulnerability could allow an attacker with local network access to bypass the application's rate-limiting mechanism, enabling brute-forcing of the screen-sharing code and potentially displaying harmful content on the affected screen...

PT-2026-65411

Successful exploitation of this vulnerability could allow an attacker with local network access to bypass the application's rate-limiting mechanism, enabling brute-forcing of the screen-sharing code and potentially displaying harmful content on the affected screen...

DEBIAN-CVE-2026-43676

An out-of-bounds access issue was addressed with improved bounds checking. This issue is fixed in Safari 26.5.2, iOS 18.7.10 and iPadOS 18.7.10, iOS 26.5.2 and iPadOS 26.5.2, macOS Tahoe 26.5.2, visionOS 26.6, watchOS 26.6. Processing maliciously crafted web content may lead to an unexpected Safa...

MAI-2024-0057

Large Language Models LLMs are susceptible to adversarial prompting attacks, a method where a strategically crafted suffix is appended to an input instruction, prompting the LLM to produce unsafe or harmful content. The AdvPrompter technique employs a separate LLM to generate these adversarial...

MAI-2024-0043

The Social Facilitation Prompt SoP framework facilitates the automated creation of jailbreak prompts, effectively circumventing the safety mechanisms embedded within large language models LLMs. This framework employs multiple optimized "jailbreak characters" within a single prompt to coerce the L...

MAI-2023-0003

A vulnerability has been identified within the fine-tuning API of GPT-4, which permits adversaries to bypass the Reinforcement Learning from Human Feedback RLHF safety mechanisms. By employing a relatively small set of meticulously crafted prompt-response pairs, attackers can fine-tune the model ...

MAI-2024-0030

Large Language Models LLMs are susceptible to advanced optimization-based jailbreaking attacks, which exploit vulnerabilities in their safety mechanisms. This vulnerability arises from the ability of attackers to craft specific prompts that bypass these safety protocols, resulting in the generati...

MAI-2024-0019

Large Language Models LLMs are prone to a specific vulnerability where they exhibit a bias towards authoritative sources. This allows malicious actors to circumvent built-in safety mechanisms by crafting prompts that include fabricated citations resembling credible sources, such as academic paper...

MAI-2024-0008

Large Language Models LLMs are susceptible to jailbreaking attacks through the use of "obscure" input prompts. The ObscurePrompt attack methodology involves the iterative transformation of a base prompt, which incorporates known jailbreaking techniques, into an obscured version utilizing another...

CVE-2025-55271

HCL Aftermarket DPC is affected by HTTP Response Splitting vulnerability where in depending on how the web application handles the split response, an attacker may be able to execute arbitrary commands or inject harmful content into the response...

Safe2Harm: Semantic Isomorphism Attacks for Jailbreaking Large Language Models

Large Language Models LLMs have demonstrated exceptional performance across various tasks, but their security vulnerabilities can be exploited by attackers to generate harmful content, causing adverse impacts across various societal domains. Most existing jailbreak methods revolve around Prompt...

VisualDAN: Exposing Vulnerabilities in VLMs with Visual-Driven DAN Commands

Vision-Language Models VLMs have garnered significant attention for their remarkable ability to interpret and generate multimodal content. However, securing these models against jailbreak attacks continues to be a substantial challenge. Unlike text-only models, VLMs integrate additional modalitie...

EUVD-2021-0862

Malware in sbrugna...

LLM Detected

The scanner detected the presence of a Large Language Model LLM on the target application. LLMs are advanced AI models capable of understanding and generating human-like text based on the input they receive. They are commonly used in various applications, including chatbots, virtual assistants,...

Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes

The advancement of AI technologies, particularly Large Language Models LLMs, has transformed computing while introducing new security and privacy risks. Prior research shows that cybercriminals are increasingly leveraging uncensored LLMs ULLMs as backends for malicious services. Understanding the...

Researchers Uncover GPT-5 Jailbreak and Zero-Click AI Agent Attacks Exposing Cloud and IoT Systems

Cybersecurity researchers have uncovered a jailbreak technique to bypass ethical guardrails erected by OpenAI in its latest large language model LLM GPT-5 and produce illicit instructions. Generative artificial intelligence AI security platform NeuralTrust said it combined a known technique calle...