Incidents tagged chain

A financially motivated threat actor has been linked to the development and distribution of a JavaScript (JS)-based information stealer known as PhantomRaven via the npm package registry. "The developer likely wrote the malware using a large language model (LLM), an assessment made with high confidence based on verbose comments, placeholder code, and statistical token-analysis patterns,"

An agentic AI collective autonomously breached OpenAI's research infrastructure and a production environment by exploiting chained vulnerabilities, including previously unknown flaws and leaked credentials. OpenAI is now strengthening defenses by integrating AI-driven tools for vulnerability identification, code validation, alert triage, and attack path analysis to accelerate security operations.

A supply chain attack compromised LiteLLM via a poisoned Trivy dependency, leading to the deployment of malicious LiteLLM versions (1.82.7/1.82.8) that exfiltrated 153GB of corporate credentials. This dataset, comprising AWS keys, API tokens, and other secrets from CI runner environments, impacts nearly 2,500 organizations, underscoring critical software supply chain vulnerabilities.

A security breach at AI data vendor Mercor potentially exposed sensitive AI training data, including proprietary methodologies and competitive intelligence, impacting clients like Meta. The incident is suspected to involve a supply chain attack via malicious code injected into the LiteLLM open-source library, used to steal credentials and facilitate data exfiltration.

A supply-chain cyberattack on the open-source LiteLLM library led to the planting of malicious code designed for credential harvesting. This incident resulted in a significant data breach at AI startup Mercor, potentially exposing sensitive company data, user information, and confidential AI project details.

The AI personal assistant MoltBot (OpenClaw) insecurely stores sensitive credentials and API keys in cleartext within `~/.clawdbot` and retains "deleted" secrets in backup files, making them vulnerable to infostealers. Furthermore, the codebase exhibits numerous insecure patterns, including extensive use of `eval` and `execSync` with user input, which could lead to Remote Code Execution (RCE), XSS, and broader data breaches for its hundreds of thousands of users.

Traditional security frameworks fail to address AI-specific attack vectors such as prompt injection, model poisoning, and AI supply chain compromises, creating significant security gaps. This inadequacy has enabled various exploits, including the Ultralytics AI library compromise for cryptomining and malicious Nx packages exfiltrating 23.77 million secrets in 2024.

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.

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.