CureIAM

CureIAM Clean up of over permissioned IAM accounts on GCP infra in an automated way CureIAM is an easy-to-use, reliable, and performant engine for Least Privilege Principle Enforcement on GCP cloud infra. It enables DevOps and Security team to quickly clean up accounts in GCP infra that have...

AGAS

An Efficient and Effective Agentic Group Shilling Attack on Recommender Systems This is the official code to the paper: "An Efficient and Effective Agentic Group Shilling Attack on Recommender Systems". This paper introduces AGAS, which is an LLM-driven shilling attack against black-box...

VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents

Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems LLM-ARS instantiate users and items as autonomous agents, whose semantic states are dynamically refined through a recurrent process known as collaborative reflection. While this mechanism improves...

PT-2026-95978

Name of the Vulnerable Software and Affected Versions SourceCodester Drug Recommendation System version 1.0 Description Cross site scripting XSS occurs when an application includes untrusted data in a web page without proper validation or escaping, allowing an attacker to execute malicious script...

PT-2026-95965

Name of the Vulnerable Software and Affected Versions SourceCodester Drug Recommendation System version 1.0 Description An issue exists in the file /drug recommender/Admin/edit user.php where improper handling of the ID argument allows for remote SQL injection. SQL injection is a technique where ...

PT-2026-95966

Name of the Vulnerable Software and Affected Versions SourceCodester Drug Recommendation System version 1.0 Description A security flaw allows remote exploitation via cross site scripting XSS, a technique where malicious scripts are injected into trusted websites. The issue exists in the file /dr...

EUVD-2019-0120

Malware in sbrugna...

Malicious code in loopback4-example-recommender (npm)

The package loopback4-example-recommender was found to contain malicious code...

MAL-2025-25597 Malicious code in loopback4-example-recommender (npm)

The package loopback4-example-recommender was found to contain malicious code...

LLM4MEA: Data-Free Model Extraction Attacks on Sequential Recommenders Via Large Language Models

Recent studies have demonstrated the vulnerability of sequential recommender systems to Model Extraction Attacks MEAs. MEAs collect responses from recommender systems to replicate their functionality, enabling unauthorized deployments and posing critical privacy and security risks. Black-box...

Phantom Subgroup Poisoning: Stealth Attacks on Federated Recommender Systems

Federated recommender systems FedRec have emerged as a promising solution for delivering personalized recommendations while safeguarding user privacy. However, recent studies have demonstrated their vulnerability to poisoning attacks. Existing attacks typically target the entire user group, which...

ImpReSS: Implicit Recommender System for Support Conversations

Following recent advancements in large language models LLMs, LLM-based chatbots have transformed customer support by automating interactions and providing consistent, scalable service. While LLM-based conversational recommender systems CRSs have attracted attention for their ability to enhance th...

SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding

Federated recommender system FedRec has emerged as a solution to protect user data through collaborative training techniques. A typical FedRec involves transmitting the full model and entire weight updates between edge devices and the server, causing significant burdens to devices with limited...

RAID: an In-Training Defense against Attribute Inference Attacks in Recommender Systems

In various networks and mobile applications, users are highly susceptible to attribute inference attacks, with particularly prevalent occurrences in recommender systems. Attackers exploit partially exposed user profiles in recommendation models, such as user embeddings, to infer private attribute...

Exploring Backdoor Attack and Defense for LLM-Empowered Recommendations

The fusion of Large Language Models LLMs with recommender systems RecSys has dramatically advanced personalized recommendations and drawn extensive attention. Despite the impressive progress, the safety of LLM-based RecSys against backdoor attacks remains largely under-explored. In this paper, we...

NuGet Package 'Microsoft.ML.Recommender' Detection

The remote host has a 'Microsoft.ML.Recommender' with a Verified NuGet package status and is installed on the remote host. Nessus has not tested for this issue but has instead relied only on the application's self-reported version number. %NASLMINLEVEL 80900 C Tenable, Inc. include'compat.inc'; i...

MaianAffiliate Cross-Site Scripting Vulnerability (CNVD-2022-62192)

MaianAffiliate v.1.0 is a free, simple but powerful php recommender system written in PHP. in the context of authenticated and unauthenticated users...

MaianAffiliate 跨站脚本漏洞

MaianAffiliate v.1.0 is a free, simple but powerful php recommender system written in PHP. in the context of authenticated and unauthenticated users...

Cross-site scripting in recommender-xblock

Recommender before 1.3.1 allows XSS. It is possible for a learner to craft a fake resource to recommender, that includes script which could possibly steal credentials from staff if they are lured into viewing the recommended resource...

GHSA-3J5X-7CCF-PPGM Cross-site scripting in recommender-xblock

Recommender before 1.3.1 allows XSS. It is possible for a learner to craft a fake resource to recommender, that includes script which could possibly steal credentials from staff if they are lured into viewing the recommended resource...