Sunglasses: Open-source AI agent input scanner launches free defense layer

AI agents are vulnerable to attacks that do not announce themselves: malicious instructions hidden inside ordinary emails, web pages, images, and documents. Traditional security focuses on authentication (OAuth, cookies, tokens) and behavior monitoring, leaving a gap in input validation—the moment before an agent sees content. Sunglasses fills that gap with a local-only, offline defense layer that does not require machine learning or cloud infrastructure. Its design philosophy is practical: it ships 1,106 patterns and 7,648 keywords across 69 attack categories, backed by 17 normalization techniques to catch evasion (Unicode confusables, leetspeak, Base64 encoding), and a "mechanism layer" that catches the structure of attacks (e.g., something sensitive + a place to exfiltrate it) so paraphrased variants still trigger.

The tool is positioned explicitly alongside existing solutions like Lakera Guard, LLM Guard, NVIDIA NeMo Guardrails, and Azure Prompt Shields. Rather than compete on ML-based detection of novel attacks, Sunglasses emphasizes local execution, zero cost, and compatibility—users can run it alone or layer it with cloud tools via an adapter system. Testing reveals a measured, honest stance on limitations: 97.4% recall on a labeled dataset of 38 real attacks plus 73 open-source READMEs as clean negatives, with 0 false positives in v0.3.1, but a known gap: the string "curl … | bash" appears legitimately in five popular README files and maliciously in attack patterns, so flagging it would add 5 false positives to catch 1 attack—a tradeoff the authors acknowledge should belong to runtime control, not text scanning.