5549 matches found
Why React Didn't Kill XSS: The New JavaScript Injection Playbook
React conquered XSS? Think again. That's the reality facing JavaScript developers in 2025, where attackers have quietly evolved their injection techniques to exploit everything from prototype pollution to AI-generated code, bypassing the very frameworks designed to keep applications secure. Full...
How Microsoft defends against indirect prompt injection attacks
Summary The growing adoption of large language models LLMs in enterprise workflows has introduced a new class of adversarial techniques: indirect prompt injection. Indirect prompt injection can be used against systems that leverage large language models LLMs to process untrusted data...
Invisible Injections: Exploiting Vision-Language Models through Steganographic Prompt Embedding
Vision-language models VLMs have revolutionized multimodal AI applications but introduce novel security vulnerabilities that remain largely unexplored. We present the first comprehensive study of steganographic prompt injection attacks against VLMs, where malicious instructions are invisibly...
Prompt Optimization and Evaluation for LLM Automated Red Teaming
Applications that use Large Language Models LLMs are becoming widespread, making the identification of system vulnerabilities increasingly important. Automated Red Teaming accelerates this effort by using an LLM to generate and execute attacks against target systems. Attack generators are evaluat...
PT-2025-31216 · Unknown · Langchain-Ai +1
Name of the Vulnerable Software and Affected Versions: langchain-ai version 0.3.51 Description: langchain-ai version 0.3.51 contains an indirect prompt injection vulnerability in the GmailToolkit component. This vulnerability allows attackers to execute arbitrary code and compromise the applicati...
CVE-2025-46059: Improper Control of Generation of Code
langchain-ai v0.3.51 was discovered to contain an indirect prompt injection vulnerability in the GmailToolkit component. This vulnerability allows attackers to execute arbitrary code and compromise the application via a crafted email message. NOTE: this is disputed by the Supplier because the...
LangChain 安全漏洞
LangChain is a LangChain open source framework for developing applications powered by the Large Language Model LLM. A security vulnerability exists in LangChain version v0.3.51, which stems from an indirect prompt injection in the GmailToolkit component that could lead to the execution of arbitra...
CVE-2025-46059
The langchain-ai library (v0.3.51) contains an indirect prompt injection vulnerability within the GmailToolkit component. Attackers can exploit this by sending a crafted email message , potentially leading to arbitrary code execution and application compromise. While the supplier disputes the cod...
Hacker Added Prompt to Amazon Q to Erase Files and Cloud Data
A hacker injected a malicious prompt into Amazon Q via GitHub, aiming to delete user files and wipe AWS data, exposing a major security flaw...
Running in CIRCLE? A Simple Benchmark for LLM Code Interpreter Security
As large language models LLMs increasingly integrate native code interpreters, they enable powerful real-time execution capabilities, substantially expanding their utility. However, such integrations introduce potential system-level cybersecurity threats, fundamentally different from prompt-based...
When LLMs Copy to Think: Uncovering Copy-Guided Attacks in Reasoning LLMs
Large Language Models LLMs have become integral to automated code analysis, enabling tasks such as vulnerability detection and code comprehension. However, their integration introduces novel attack surfaces. In this paper, we identify and investigate a new class of prompt-based attacks, termed...
EX-NIDS: a Framework for Explainable Network Intrusion Detection Leveraging Large Language Models
This paper introduces eX-NIDS, a framework designed to enhance interpretability in flow-based Network Intrusion Detection Systems NIDS by leveraging Large Language Models LLMs. In our proposed framework, flows labelled as malicious by NIDS are initially processed through a module called the Promp...
PT-2025-30404 · Undefined · Undefined
CVE-2025-54356 Rejected reason https://t.co/wlhPkGwe6f Don't wait vulnerability scanning results: https://t.co/oh1APvMMnd...
DREAM: Scalable Red Teaming for Text-To-Image Generative Systems Via Distribution Modeling
Despite the integration of safety alignment and external filters, text-to-image T2I generative models are still susceptible to producing harmful content, such as sexual or violent imagery. This raises serious concerns about unintended exposure and potential misuse. Red teaming, which aims to...
Chaindesk 跨站脚本漏洞
Chaindesk is an AI chatbot for building and deploying private data-based chatbots from Chaindesk, France. A cross-site scripting vulnerability exists in Chaindesk version 2025-05-26 and earlier, which stems from a system prompt in the AI agent that can embed a malicious script payload, leading to...
Talking like a Phisher: LLM-Based Attacks on Voice Phishing Classifiers
Voice phishing vishing remains a persistent threat in cybersecurity, exploiting human trust through persuasive speech. While machine learning ML-based classifiers have shown promise in detecting malicious call transcripts, they remain vulnerable to adversarial manipulations that preserve semantic...
git: Git does not sanitize URLs when asking for credentials interactively
A flaw was found in Git. This vulnerability occurs when Git requests credentials via a terminal prompt, for example, without the use of a credential helper. During this process, Git displays the host name for which the credentials are needed, but any URL-encoded parts are decoded and displayed...
TelegAI Cross Site Scripting
TelegAI, a web application for constructing and chatting with AI Characters, is vulnerable to persistent cross site scripting vulnerabilities in its chat component and character container component. An attacker can achieve arbitrary client-side script execution by crafting an AI Character with SV...
PromptArmor: Simple yet Effective Prompt Injection Defenses
Despite their potential, recent research has demonstrated that LLM agents are vulnerable to prompt injection attacks, where malicious prompts are injected into the agent's input, causing it to perform an attacker-specified task rather than the intended task provided by the user. In this paper, we...
