CVE-2025-5074: Buffer Copy without Checking Size of Input

A vulnerability, which was classified as critical, was found in FreeFloat FTP Server 1.0. Affected is an unknown function of the component PROMPT Command Handler. The manipulation leads to buffer overflow. It is possible to launch the attack remotely. The exploit has been disclosed to the public...

CVE-1999-0159

Attackers can crash a Cisco IOS router or device, provided they can get to an interactive prompt such as a login. This applies to some IOS 9.x, 10.x, and 11.x releases...

📄 Remote for Windows 2024.15 Remote Code Execution

Remote for Windows version 2024.15 suffers from multiple remote code execution vulnerabilities. Exploit Title: Remote for Windows 2024.15 - RCE Date: 2025-05-19 Exploit Author: Chokri Hammedi Vendor Homepage: https://rs.ltd Software Link: https://rs.ltd/latest.php?os=win Version: 2024.15 Tested o...

Alignment under Pressure: the Case for Informed Adversaries When Evaluating LLM Defenses

Large language models LLMs are rapidly deployed in real-world applications ranging from chatbots to agentic systems. Alignment is one of the main approaches used to defend against attacks such as prompt injection and jailbreaks. Recent defenses report near-zero Attack Success Rates ASR even again...

Is Your Prompt Safe? Investigating Prompt Injection Attacks against Open-Source LLMs

Whitepaper called Is Your Prompt Safe? Investigating Prompt Injection Attacks Against Open-Source LLMs...

Lessons from Defending Gemini against Indirect Prompt Injections

Gemini is increasingly used to perform tasks on behalf of users, where function-calling and tool-use capabilities enable the model to access user data. Some tools, however, require access to untrusted data introducing risk. Adversaries can embed malicious instructions in untrusted data which caus...

Beyond Text: Unveiling Privacy Vulnerabilities in Multi-Modal Retrieval-Augmented Generation

Multimodal Retrieval-Augmented Generation MRAG systems enhance LMMs by integrating external multimodal databases, but introduce unexplored privacy vulnerabilities. While text-based RAG privacy risks have been studied, multimodal data presents unique challenges. We provide the first systematic...

Can Large Language Models Really Recognize Your Name?

Large language models LLMs are increasingly being used to protect sensitive user data. However, current LLM-based privacy solutions assume that these models can reliably detect personally identifiable information PII, particularly named entities. In this paper, we challenge that assumption by...

Your First Spring AI 1.0 Application

Your First Spring AI 1.0 Application by Dr. Mark Pollack, Christian Tsolov, and Josh Long Hi, Spring fans! Spring AI is live on the Spring Initializr and everywhere fine bytes might be had. Ask your doctor if AI is right for you! It's an amazing time to be a Java and Spring developer. There's nev...

The Hidden Dangers of Browsing AI Agents

Autonomous browsing agents powered by large language models LLMs are increasingly used to automate web-based tasks. However, their reliance on dynamic content, tool execution, and user-provided data exposes them to a broad attack surface. This paper presents a comprehensive security evaluation of...

Improving LLM Outputs against Jailbreak Attacks with Expert Model Integration

Using LLMs in a production environment presents security challenges that include vulnerabilities to jailbreaks and prompt injections, which can result in harmful outputs for humans or the enterprise. The challenge is amplified when working within a specific domain, as topics generally accepted fo...

ProxyPrompt: Securing System Prompts against Prompt Extraction Attacks

The integration of large language models LLMs into a wide range of applications has highlighted the critical role of well-crafted system prompts, which require extensive testing and domain expertise. These prompts enhance task performance but may also encode sensitive information and filtering...

AutoRAN: Weak-To-Strong Jailbreaking of Large Reasoning Models

This paper presents AutoRAN, the first automated, weak-to-strong jailbreak attack framework targeting large reasoning models LRMs. At its core, AutoRAN leverages a weak, less-aligned reasoning model to simulate the target model's high-level reasoning structures, generates narrative prompts, and...

WASP: Benchmarking Web Agent Security against Prompt Injection Attacks

Autonomous UI agents powered by AI have tremendous potential to boost human productivity by automating routine tasks such as filing taxes and paying bills. However, a major challenge in unlocking their full potential is security, which is exacerbated by the agent's ability to take action on their...

Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data

The integration of large language models LLMs into cyber security applications presents significant opportunities, such as enhancing threat analysis and malware detection, but can also introduce critical risks and safety concerns, including personal data leakage and automated generation of new...

DataSentinel: a Game-Theoretic Detection of Prompt Injection Attacks

LLM-integrated applications and agents are vulnerable to prompt injection attacks, where an attacker injects prompts into their inputs to induce attacker-desired outputs. A detection method aims to determine whether a given input is contaminated by an injected prompt. However, existing detection...

FreeBSD : vscode -- security feature bypass vulnerability (6f10b49d-07b1-4be4-8abf-edf880b16ad2)

The version of FreeBSD installed on the remote host is prior to tested version. It is, therefore, affected by a vulnerability as referenced in the 6f10b49d-07b1-4be4-8abf-edf880b16ad2 advisory. VSCode developers report: A security feature bypass vulnerability exists in VS Code 1.100.0 and earlier...

GenAI Security: Outsmarting the Bots with a Proactive Testing Framework

The increasing sophistication and integration of Generative AI GenAI models into diverse applications introduce new security challenges that traditional methods struggle to address. This research explores the critical need for proactive security measures to mitigate the risks associated with...

PCMan FTP Server PROMPT Command Handler Buffer Overflow Vulnerability

PCMan FTP Server is PCMan open source set of FTP server software. PCMan FTP Server suffers from a buffer overflow vulnerability that originates from the PROMPT command handler failing to properly validate the length of input data, which can be exploited by an attacker to cause a denial of service...

Red Teaming the Mind of the Machine: a Systematic Evaluation of Prompt Injection and Jailbreak Vulnerabilities in LLMs

Large Language Models LLMs are increasingly integrated into consumer and enterprise applications. Despite their capabilities, they remain susceptible to adversarial attacks such as prompt injection and jailbreaks that override alignment safeguards. This paper provides a systematic investigation o...