CVE-2026-2473

Predictable bucket naming in Vertex AI Experiments in Google Cloud Vertex AI from version 1.21.0 up to but not including 1.133.0 on Google Cloud Platform allows an unauthenticated remote attacker to achieve cross-tenant remote code execution, model theft, and poisoning via pre-creating predictabl...

Generation of Predictable Numbers or Identifiers

Overview google-cloud-aiplatform is a Vertex AI API client library Affected versions of this package are vulnerable to Generation of Predictable Numbers or Identifiers for Cloud Storage buckets. An attacker can execute code remotely, steal models, or poison data by pre-creating buckets with...

CVE-2025-69207

Khoj is a self-hostable artificial intelligence app. Prior to 2.0.0-beta.23, an IDOR in the Notion OAuth callback allows an attacker to hijack any user's Notion integration by manipulating the state parameter. The callback endpoint accepts any user UUID without verifying the OAuth flow was...

CVE-2025-69207

Khoj is a self-hostable artificial intelligence app. Prior to 2.0.0-beta.23, an IDOR in the Notion OAuth callback allows an attacker to hijack any user's Notion integration by manipulating the state parameter. The callback endpoint accepts any user UUID without verifying the OAuth flow was...

CVE-2025-69207

CVE-2025-69207 concerns Khoj, a self-hosted AI app. Pre-2.0.0-beta.23, an IDOR in the Notion OAuth callback lets an attacker hijack a user’s Notion integration by manipulating the OAuth callback’s state parameter. The endpoint accepts any user UUID without verifying the initiated OAuth flow, enab...

CVE-2025-69207

Khoj is a self-hostable artificial intelligence app. Prior to 2.0.0-beta.23, an IDOR in the Notion OAuth callback allows an attacker to hijack any user's Notion integration by manipulating the state parameter. The callback endpoint accepts any user UUID without verifying the OAuth flow was...

CVE-2025-69207 Khoj has an IDOR in Notion OAuth Flow Enables Index Poisoning

Khoj is a self-hostable artificial intelligence app. Prior to 2.0.0-beta.23, an IDOR in the Notion OAuth callback allows an attacker to hijack any user's Notion integration by manipulating the state parameter. The callback endpoint accepts any user UUID without verifying the OAuth flow was...

CVE-2025-69207 Khoj has an IDOR in Notion OAuth Flow Enables Index Poisoning

Khoj is a self-hostable artificial intelligence app. Prior to 2.0.0-beta.23, an IDOR in the Notion OAuth callback allows an attacker to hijack any user's Notion integration by manipulating the state parameter. The callback endpoint accepts any user UUID without verifying the OAuth flow was...

Khoj has an IDOR in Notion OAuth Flow that Enables Index Poisoning

Summary An IDOR in the Notion OAuth callback allows an attacker to hijack any user's Notion integration by manipulating the state parameter. The callback endpoint accepts any user UUID without verifying the OAuth flow was initiated by that user, allowing attackers to replace victims' Notion...

GHSA-6WHJ-7QMG-86QJ Khoj has an IDOR in Notion OAuth Flow that Enables Index Poisoning

Summary An IDOR in the Notion OAuth callback allows an attacker to hijack any user's Notion integration by manipulating the state parameter. The callback endpoint accepts any user UUID without verifying the OAuth flow was initiated by that user, allowing attackers to replace victims' Notion...

PT-2026-5703

Name of the Vulnerable Software and Affected Versions Khoj versions prior to 2.0.0-beta.23 Description Khoj is an artificial intelligence application that is self-hostable. A flaw exists in the Notion OAuth callback functionality that allows an attacker to take control of any user's Notion...

A new era of agents, a new era of posture

The rise of AI Agents marks one of the most exciting shifts in technology today. Unlike traditional applications or cloud resources, these agents are not passive components- they reason, make decisions, invoke tools, and interact with other agents and systems on behalf of users. This autonomy...

Corrupting LLMs Through Weird Generalizations

Fascinating research: Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs. Abstract LLMs are useful because they generalize so well. But can you have too much of a good thing? We show that a small amount of finetuning in narrow contexts can dramatically shift behavior outside...

Medium: unbound

Issue Overview: NLnet Labs Unbound up to and including version 1.24.0 is vulnerable to possible domain hijack attacks. Promiscuous NS RRSets that complement positive DNS replies in the authority section can be used to trick resolvers to update their delegation information for the zone. Usually...

FedPoisonTTP: A Threat Model and Poisoning Attack for Federated Test-Time Personalization

Test-time personalization in federated learning enables models at clients to adjust online to local domain shifts, enhancing robustness and personalization in deployment. Yet, existing federated learning work largely overlooks the security risks that arise when local adaptation occurs at test tim...

Steganographic Backdoor Attacks in NLP: Ultra-Low Poisoning and Defense Evasion

Transformer models are foundational to natural language processing NLP applications, yet remain vulnerable to backdoor attacks introduced through poisoned data, which implant hidden behaviors during training. To strengthen the ability to prevent such compromises, recent research has focused on...

Data Poisoning Vulnerabilities across Healthcare AI Architectures: A Security Threat Analysis

Healthcare AI systems face major vulnerabilities to data poisoning that current defenses and regulations cannot adequately address. We analyzed eight attack scenarios in four categories: architectural attacks on convolutional neural networks, large language models, and reinforcement learning...

RAG-Targeted Adversarial Attack on LLM-Based Threat Detection and Mitigation Framework

The rapid expansion of the Internet of Things IoT is reshaping communication and operational practices across industries, but it also broadens the attack surface and increases susceptibility to security breaches. Artificial Intelligence has become a valuable solution in securing IoT networks, wit...

SecureLearn - an Attack-Agnostic Defense for Multiclass Machine Learning against Data Poisoning Attacks

Data poisoning attacks are a potential threat to machine learning ML models, aiming to manipulate training datasets to disrupt their performance. Existing defenses are mostly designed to mitigate specific poisoning attacks or are aligned with particular ML algorithms. Furthermore, most defenses a...

You can poison AI with just 250 dodgy documents

Researchers have shown how you can corrupt an AI and make it talk gibberish by tampering with just 250 documents. The attack, which involves poisoning the data that an AI trains on, is the latest in a long line of research that has uncovered vulnerabilities in AI models. Anthropic which produces...