CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Models

Retrieval-Augmented Generation RAG enhances large language models LLMs by incorporating external knowledge, but its openness introduces vulnerabilities that can be exploited by poisoning attacks. Existing poisoning methods for RAG systems have limitations, such as poor generalization and lack of...

CVE-2024-48509

Learning with Texts LWT 2.0.3 is vulnerable to SQL Injection. This occurs when the application fails to properly sanitize user inputs, allowing attackers to manipulate SQL queries by injecting malicious SQL statements into URL parameters. By exploiting this vulnerability, an attacker could gain...

CVE-2024-3072

The ACF Front End Editor plugin for WordPress is vulnerable to unauthorized modification of data due to a missing capability check on the updatetexts function in all versions up to, and including, 2.0.2. This makes it possible for authenticated attackers, with subscriber-level access and above, t...

CVE-2023-46775

Cross-Site Request Forgery CSRF vulnerability in Djo Original texts Yandex WebMaster plugin = 1.18 versions...

CVE-2004-2237

Unknown vulnerability in Moodle before 1.3.4 has unknown impact and attack vectors, related to "strings in Moodle texts."...

How Private Investigators Handle Digital Forensics?

The world we live in is packed with data. Texts, emails, social media posts, deleted files, you name…...

Traceback of Poisoning Attacks to Retrieval-Augmented Generation

Large language models LLMs integrated with retrieval-augmented generation RAG systems improve accuracy by leveraging external knowledge sources. However, recent research has revealed RAG's susceptibility to poisoning attacks, where the attacker injects poisoned texts into the knowledge database,...

[SECURITY] Fedora 42 Update: dokuwiki-20240206b-1.fc42

DokuWiki is a standards compliant, simple to use Wiki, mainly aimed at creati ng documentation of any kind. It has a simple but powerful syntax which makes su re the data-files remain readable outside the Wiki and eases the creation of structured texts. All data is stored in plain text files no...

CVE-2024-12460

The Years Since – Timeless Texts plugin for WordPress is vulnerable to Stored Cross-Site Scripting via the plugin's 'years-since' shortcode in all versions up to, and including, 1.4.1 due to insufficient input sanitization and output escaping on user supplied attributes. This makes it possible fo...

WordPress plugin Years Since – Timeless Texts 跨站脚本漏洞

WordPress and WordPress plugin are both products of the WordPress Foundation. WordPress is a blogging platform developed in the PHP language. WordPress is a blogging platform developed using the PHP language, which supports personal blogs on PHP and MySQL servers. A cross-site scripting...

CVE-2024-12460: Improper Neutralization of Input During Web Page Generation

The Years Since – Timeless Texts plugin for WordPress is vulnerable to Stored Cross-Site Scripting via the plugin's 'years-since' shortcode in all versions up to, and including, 1.4.1 due to insufficient input sanitization and output escaping on user supplied attributes. This makes it possible fo...

PT-2025-10410

Name of the Vulnerable Software and Affected Versions The Years Since – Timeless Texts plugin for WordPress versions up to, and including, 1.4.1 Description The issue arises from insufficient input sanitization and output escaping on user-supplied attributes in the plugin's 'years-since' shortcod...

CVE-2024-48509

Learning with Texts LWT 2.0.3 is vulnerable to SQL Injection. This occurs when the application fails to properly sanitize user inputs, allowing attackers to manipulate SQL queries by injecting malicious SQL statements into URL parameters. By exploiting this vulnerability, an attacker could gain...

CVE-2024-48509

Learning with Texts LWT 2.0.3 is vulnerable to SQL Injection. This occurs when the application fails to properly sanitize user inputs, allowing attackers to manipulate SQL queries by injecting malicious SQL statements into URL parameters. By exploiting this vulnerability, an attacker could gain...

CVE-2024-48509

CVE-2024-48509 affects Learning with Texts (LWT) 2.0.3. Multiple sources describe a SQL Injection vulnerability caused by inadequate input sanitization in URL parameters, enabling an attacker to manipulate queries and potentially access, modify, or delete data and execute arbitrary commands. The ...

PT-2024-33127 · Unknown · Learning With Texts

Name of the Vulnerable Software and Affected Versions: Learning with Texts LWT version 2.0.3 Description: The issue occurs due to the application's failure to properly sanitize user inputs, allowing attackers to manipulate SQL queries by injecting malicious SQL statements into URL parameters. Thi...

CVE-2024-48509

Learning with Texts LWT 2.0.3 is vulnerable to SQL Injection. This occurs when the application fails to properly sanitize user inputs, allowing attackers to manipulate SQL queries by injecting malicious SQL statements into URL parameters. By exploiting this vulnerability, an attacker could gain...

CVE-2024-48509: Improper Neutralization of Special Elements used in an SQL Command

Learning with Texts LWT 2.0.3 is vulnerable to SQL Injection. This occurs when the application fails to properly sanitize user inputs, allowing attackers to manipulate SQL queries by injecting malicious SQL statements into URL parameters. By exploiting this vulnerability, an attacker could gain...

Learning with Texts SQL注入漏洞

Learning with Texts LWT is a software application by the individual developer Jon Gauthier. It allows users to import text, read, save, view and test words and expressions in multiple languages. A security vulnerability exists in Learning with Texts LWT version 2.0.3, which stems from...

CVE-2024-48509

Learning with Texts LWT 2.0.3 is vulnerable to SQL Injection. This occurs when the application fails to properly sanitize user inputs, allowing attackers to manipulate SQL queries by injecting malicious SQL statements into URL parameters. By exploiting this vulnerability, an attacker could gain...