AI lectures with Dr. Brumley Part 3 | Securing the AI attack surface

AI systems present an expanded attack surface shaped by an attacker’s level of model access, position in the ecosystem, and objective, spanning direct users, malicious retrieved content, and compromised supply-chain components. Classical machine-learning threats remain relevant, including evasion attacks that manipulate inputs, training-data poisoning, and model theft through repeated queries. Large language models add prompt injection as a central risk because developer instructions, user inputs, and retrieved data share the same context, enabling malicious text in sources such as metadata, web pages, or GitHub issues to influence model behavior. Examples described include indirect prompt injection leading to Docker-related remote code execution and data exfiltration, many-shot jailbreaking through context manipulation, tool abuse using a user’s authenticated permissions, and persistent memory poisoning that can affect future conversations. Effective mitigation requires defense in depth, including robust ML techniques, guardrail classifiers, validated and allowlisted tool schemas, sandboxed execution, artifact provenance controls, output verification, and human approval for consequential actions, since security enforcement should reside in surrounding system controls rather than the model alone.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.