Initial Access: MITRE ATLAS

Adversaries may gain initial access to a system by compromising the unique portions of the AI supply chain. This could include Hardware, Data and its annotations, parts of the AI AI Software stack, or the Model itself. In some instances the attacker will need secondary access to fully carry out an attack using compromised components of the supply chain.

Adversaries may obtain and abuse credentials of existing accounts as a means of gaining Initial Access. Credentials may take the form of usernames and passwords of individual user accounts or API keys that provide access to various AI resources and services.

Adversaries can Craft Adversarial Data that prevents an AI model from correctly identifying the contents of the data or Generate Deepfakes that fools an AI model expecting authentic data.

Adversaries may attempt to take advantage of a weakness in an Internet-facing computer or program using software, data, or commands in order to cause unintended or unanticipated behavior. The weakness in the system can be a bug, a glitch, or a design vulnerability. These applications are often websites, but can include databases (like SQL), standard services (like SMB or SSH), network device administration and management protocols (like SNMP and Smart Install), and any other applications with Internet accessible open sockets, such as web servers and related services.

Adversaries may send phishing messages to gain access to victim systems. All forms of phishing are electronically delivered social engineering. Phishing can be targeted, known as spearphishing. In spearphishing, a specific individual, company, or industry will be targeted by the adversary. More generally, adversaries can conduct non-targeted phishing, such as in mass malware spam campaigns.

Adversaries may gain access to an AI system through a user visiting a website over the normal course of browsing, or an AI agent retrieving information from the web on behalf of a user. Websites can contain an LLM Prompt Injection which, when executed, can change the behavior of the AI model.

An adversary may introduce malicious prompts into the victim's system via a public-facing application with the intention of it being ingested by an AI at some point in the future and ultimately having a downstream effect. This may occur when a data source is indexed by a retrieval augmented generation (RAG) system, when a rule triggers an action by an AI agent, or when a user utilizes a large language model (LLM) to interact with the malicious content. The malicious prompts may persist on the victim system for an extended period and could affect multiple users and various AI tools within the victim organization.