MAL-2025-191789 Malicious code in mcp-weather-full (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 c12eff5425b0aa04547b3bbff3444c1d96ca3cf765fdc105d7b7ff9252c9afda Package seems to provide an MCP server, but in fact contains attempts to make an LLM agent break safeguards. As the request is about leaves just a flag, it see...

From Model to Breach: Towards Actionable LLM-Generated Vulnerabilities Reporting

As the role of Large Language Models LLM-based coding assistants in software development becomes more critical, so does the role of the bugs they generate in the overall cybersecurity landscape. While a number of LLM code security benchmarks have been proposed alongside approaches to improve the...

Malicious code in wayspiritmcp-tpa (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 523cbbda7a0fda2addfcd432b1bfcc1df072ee67a593ffce535b7da7005caae8 Package seems to provide an MCP server, but in fact contains attempts to make an LLM agent break safeguards. As the request is about leaves just a flag, it see...

MAL-2025-191924 Malicious code in wayspiritmcp-enconly (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 b075eb7116e55dd48db0e026ce51a42ec4e7e1e100b4b68c8a42d4b35411f749 Package seems to provide an MCP server, but in fact contains attempts to make an LLM agent break safeguards. As the request is about leaves just a flag, it see...

Malicious code in wayspiritmcp-weather (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 c3dbe830c7b2364daef2e4634c16062b86b0b26b88f95533e9413aa91bc646fd Package seems to provide an MCP server, but in fact contains attempts to make an LLM agent break safeguards. As the request is about leaves just a flag, it see...

Hybrid Fuzzing with LLM-Guided Input Mutation and Semantic Feedback

Software fuzzing has become a cornerstone in automated vulnerability discovery, yet existing mutation strategies often lack semantic awareness, leading to redundant test cases and slow exploration of deep program states. In this work, I present a hybrid fuzzing framework that integrates static an...

CVE-2025-64318

Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Mulesoft Anypoint Code Builder allows Manipulating Writeable Configuration Files.This issue affects Mulesoft Anypoint Code Builder: before 1.12.1...

CVE-2025-64320

Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Agentforce Vibes Extension allows Code Injection.This issue affects Agentforce Vibes Extension: before 3.2.0...

CVE-2025-10875

Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Mulesoft Anypoint Code Builder allows Code Injection.This issue affects Mulesoft Anypoint Code Builder: before 1.11.6...

CVE-2025-64320

Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Agentforce Vibes Extension allows Code Injection.This issue affects Agentforce Vibes Extension: before 3.2.0...

CVE-2025-64318

Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Mulesoft Anypoint Code Builder allows Manipulating Writeable Configuration Files.This issue affects Mulesoft Anypoint Code Builder: before 1.12.1...

CVE-2025-10875

Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Mulesoft Anypoint Code Builder allows Code Injection.This issue affects Mulesoft Anypoint Code Builder: before 1.11.6...

PT-2025-45033

Name of the Vulnerable Software and Affected Versions Salesforce Agentforce Vibes Extension versions prior to 3.2.0 Description An issue exists in Salesforce Agentforce Vibes Extension related to improper neutralization of input used for LLM prompting, which can lead to code injection. The issue...

Jailbreaking in the Haystack

Recent advances in long-context language models LMs have enabled million-token inputs, expanding their capabilities across complex tasks like computer-use agents. Yet, the safety implications of these extended contexts remain unclear. To bridge this gap, we introduce NINJA short for...

Aether - Adaptive Exploit and Threat Hunting Engine for EVM-based Repositories

Aether is a Python-based framework for analyzing Solidity smart contracts, generating vulnerability findings, producing Foundry-based proof-of-concept PoC tests, and optionally validating those tests on mainnet forks. It combines static analysis, prompt-driven LLM analysis, and AI-ensemble...

AI Summarization Optimization

These days, the most important meeting attendee isn’t a person: It’s the AI notetaker. This system assigns action items and determines the importance of what is said. If it becomes necessary to revisit the facts of the meeting, its summary is treated as impartial evidence. But clever meeting...

Important: Red Hat Security Advisory: Red Hat Enterprise Linux AI 1.5 (NVIDIA)

Red Hat Enterprise Linux AI 1.5 NVIDIA is now available. Red Hat® Enterprise Linux® AI is a foundation model platform to seamlessly develop, test, and run Granite family large language models LLMs for enterprise applications...

Scam Shield: Multi-Model Voting and Fine-Tuned LLMs against Adversarial Attacks

Scam detection remains a critical challenge in cybersecurity as adversaries craft messages that evade automated filters. We propose a Hierarchical Scam Detection System HSDS that combines a lightweight multi-model voting front end with a fine-tuned LLaMA 3.1 8B Instruct back end to improve accura...

LLM-Enabled Espionage : The AI assistant that moonlights as a mole

Running short on time but still want to stay in the know? Well, we’ve got you covered! We’ve condensed all the key takeaways into a handy audio summary. It began as a low-priority alert from the SOC: an AI assistant accessed an internal finance folder at 2:14 AM. No credentials were stolen. No...

Network Intrusion Detection: Evolution from Conventional Approaches to LLM Collaboration and Emerging Risks

This survey systematizes the evolution of network intrusion detection systems NIDS, from conventional methods such as signature-based and neural network NN-based approaches to recent integrations with large language models LLMs. It clearly and concisely summarizes the current status, strengths, a...