Incidents tagged in

AI browsers are highly susceptible to prompt injection attacks, where threat actors can manipulate Large Language Models (LLMs) to bypass security controls and execute unauthorized actions. This vulnerability allows for sensitive data exfiltration by exploiting the LLM's inability to distinguish between trusted user commands and malicious instructions embedded in untrusted web content, effectively nullifying browser protections like the same-origin policy.

The article highlights critical security risks in Large Language Model (LLM) deployments, emphasizing prompt injection as a key attack vector where malicious inputs override an LLM's intended behavior. This can lead to sensitive data leakage, unauthorized actions, or the generation of harmful content, necessitating robust input validation and secure model deployment practices.

The NVIDIA Container Toolkit contains a critical security flaw, identified as CVE-2024-0132, which allows for container escape. This vulnerability grants attackers full host system access, highlighting a significant risk in AI infrastructure.

Researchers demonstrated that as few as 250 poisoned documents can create a backdoor vulnerability in large language models, irrespective of model size or training data volume. This data poisoning technique, which can induce denial-of-service or potentially facilitate data exfiltration, challenges prior assumptions about the required scale of malicious training data during pretraining.

Attackers can achieve remote code execution (RCE) on developer machines by leveraging indirect prompt injection against agentic AI developer tools. This is accomplished by introducing untrusted data, such as malicious commands in GitHub issues or hidden payloads in fake Python packages within pull requests, which the AI agent autonomously executes.

Threat actors are employing Large Language Models (LLMs) to create sophisticated phishing campaigns, leveraging LLM-generated code to obfuscate malicious payloads within Scalable Vector Graphics (SVG) files. These meticulously crafted SVG files bypass email security by disguising embedded JavaScript with business terminology, ultimately redirecting victims to fake login pages for credential harvesting.

Salesforce Agentforce was susceptible to a critical indirect prompt injection vulnerability, codenamed ForcedLeak (CVSS 9.4). This flaw allowed attackers to exfiltrate sensitive CRM data by manipulating Web-to-Lead forms, causing AI agents to transmit information to an attacker-controlled domain.

A multi-stage supply chain attack, tracked as UNC6395, originated from the compromise of a Salesloft GitHub repository, leading to the theft of a sensitive OAuth token. This token enabled access to a high-privilege AI chatbot application (Drift) and subsequent exfiltration of customer conversation data and contact information from over 700 integrated Salesforce instances.

A security flaw in the Cursor AI code editor, a Visual Studio Code fork, allows for silent arbitrary code execution when a maliciously crafted repository is opened. This vulnerability stems from "Workspace Trust" being disabled by default, enabling tasks configured in `.vscode/tasks.json` to auto-execute and posing a significant supply chain risk.

Hackers exploited a vulnerable workflow in the Nx build system to achieve code injection and GITHUB_TOKEN theft, enabling the publication of malicious package versions. These malicious packages deployed a post-install script that exfiltrated thousands of sensitive credentials and notably weaponized AI assistants like Claude and Gemini for reconnaissance and data exfiltration.

Threat actor UNC6395 exploited compromised OAuth and refresh tokens associated with the Drift AI chat agent, accessible via Salesloft, to gain unauthorized access to Salesforce customer instances. This systematic campaign led to the exfiltration of sensitive data, including AWS access keys, passwords, and Snowflake tokens, from over 700 organizations, indicating a potential supply chain attack.

AI coding tools like Claude Code integrate security features to identify common vulnerabilities such as SQL injection, XSS, RCE, and SSRF during development workflows. However, these tools are limited by their training data and struggle to detect novel, complex, or architectural vulnerabilities, potentially instilling a dangerous false sense of security among developers.

Lenovo's GPT-4-powered chatbot "Lena" was vulnerable to cross-site scripting (XSS) attacks due to improper input and output sanitization, initiated by a malicious prompt. This allowed attackers to steal session cookies and potentially gain unauthorized access to customer support systems, facilitating further system command execution and lateral movement.

The article highlights novel prompt injection techniques, such as ASCII Smuggling and hidden instructions in public code repositories, designed to be imperceptible to human developers but interpretable by LLM-powered coding agents. These sophisticated methods exploit the agents' access to external data to achieve Remote Code Execution (RCE) on developer systems, especially concerning when agents operate in unconfirmed execution modes like 'Auto-Run.'

The article details critical security risks inherent in Large Language Models (LLMs), prominently featuring prompt injection as an exploit where attackers manipulate inputs to override model instructions and elicit unintended actions. It also emphasizes sensitive data leakage, noting that LLMs can expose proprietary or private information, either directly from training data or through malicious outputs.

Enterprise AI assistants have been identified as vulnerable to abuse, potentially enabling unauthorized data theft. This exploitation pathway also allows for the manipulation of enterprise data or the behavior of these AI systems.

A critical vulnerability in the AI vibe coding platform Base44 allowed unauthorized access to private applications by exploiting unauthenticated registration and email verification endpoints. Attackers could use a publicly available `app_id` to create verified accounts, bypassing authentication controls including SSO and granting full access to sensitive enterprise data.

The Amazon Q Developer Extension for Visual Studio Code (version 1.84.0) was compromised via a software supply chain attack, embedding a prompt injection that bypassed security reviews. This malicious prompt, detailed in AWS Security Bulletin AWS-2025-015, instructed the AI assistant to systematically delete local file systems and AWS cloud resources, including S3 buckets, EC2 instances, and IAM users.

Evidence indicates that SQLite is affected by a security issue. Reported affected versions include 3.50.2.

A critical security flaw in McDonald's McHire AI hiring tool leveraged default '123456' administrative credentials combined with an Insecure Direct Object Reference (IDOR) vulnerability in an internal API. This exploit allowed researchers to access and potentially expose sensitive Personally Identifiable Information (PII) for millions of job applicants, including chat histories and contact details.