Incidents tagged Inject

LLM-based applications are susceptible to remote code execution (RCE) vulnerabilities when executing LLM-generated code via functions like `exec` or `eval` without proper sandboxing, often triggered by prompt injection. Additionally, insecure access controls in Retrieval-Augmented Generation (RAG) systems can lead to data leakage and indirect prompt injection, while active content rendering of LLM outputs enables data exfiltration by embedding malicious links or images.

The article details critical security challenges associated with cloud-hosted Large Language Models (LLMs), including prompt injection, adversarial exploits, model jailbreaks, sensitive data leakage, and misconfigurations. These vulnerabilities, stemming from lack of visibility and ungoverned AI behavior, necessitate proactive discovery and risk management to safeguard AI workloads.

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.'

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.

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.

The article highlights critical security risks in AI and LLM deployments, specifically prompt injection and jailbreak attacks, which enable manipulation for unauthorized actions, sensitive data exposure, and compliance failures. These rapid exploits, alongside data leakage and model theft, pose significant financial and reputational impacts on enterprises leveraging AI technologies.

The article highlights critical security gaps in Large Language Model (LLM) applications, detailing common vulnerabilities such as prompt injection, sensitive information disclosure, and supply chain compromises. These flaws, categorized by the OWASP Top 10 for LLM Applications, can lead to unintended LLM behavior, data exposure, and other serious consequences.

In Langchain before 0.0.329, prompt injection allows an attacker to force the service to retrieve data from an arbitrary URL, essentially providing SSRF and potentially injecting content into downstream tasks.

SQL injection vulnerability in langchain allows a remote attacker to obtain sensitive information via the SQLDatabaseChain component.