Security considerations for LLM applications
LLMs introduce new security challenges that traditional web or application security measures weren’t designed to handle. Standard controls often fail against attacks unique to LLMs, and recent incidents—from prompt leaking in commercial chatbots to hallucinated legal citations—highlight the need for dedicated defenses.
LLM applications differ fundamentally from conventional software because they accept both system instructions and user data through the same text channel, produce nondeterministic outputs, and manage context in ways that can expose or mix up sensitive information. For example, attackers have extracted hidden system prompts by simply asking some models to repeat their instructions, and firms have suffered from models inventing fictitious legal precedents. Moreover, simple pattern‐matching filters can be bypassed by cleverly rephrased malicious inputs, making semantic‐aware defenses essential...
