The primary security threat from AI is no longer just generating bad content. It's the risk of an AI agent, tricked by malicious input, taking harmful actions like deleting databases or leaking files using its legitimate system privileges.
Unlike direct attacks where users type malicious commands, indirect prompt injection occurs when an AI agent processes untrusted data (like an email or webpage) containing hidden instructions, causing it to perform unintended actions on the attacker's behalf.
Traditional Web Application Firewalls (WAFs) detect code-based attacks by looking for known signatures. They are ineffective against AI attacks, which can be crafted in plain, natural language that is invisible to these legacy security tools.
Attackers can corrupt an autonomous agent's long-term behavior by strategically feeding it misleading or malicious information over time. This "context poisoning" embeds false instructions into the agent's memory, altering how it handles future tasks months later.
An AI-BOM is an inventory of all system components, including model versions, datasets, and third-party plugins. Similar to a Software Bill of Materials (SBOM), it is essential for tracking vulnerabilities within the AI supply chain when underlying components are compromised.