The landscape of AI agents has been dominated by large language models (LLMs) like GPT-4 and Claude, but a new frontier is opening up: lightweight, open-source, locally-deployable agents that can run on consumer hardware. This post shares internal notes and discoveries from my journey building agents for small language models (SLMs) – models ranging from 270M to 32B parameters that run efficiently on CPUs or modest GPUs. These are lessons learned from hands-on experimentation, debugging, and optimizing inference pipelines.

The Genesis: When Signatures Aren’t Enough 🔗In the world of mobile security research, there’s a recurring frustration that keeps many of us up at night: the most sophisticated exploits - the ones that really matter - are rarely shared. When Citizen Lab and Google TAG discover NSO Group’s latest 0-click exploits targeting journalists and activists, we get brilliant technical writeups, CVE numbers, and patches. What we don’t get? The actual samples. This isn’t a criticism - there are excellent reasons for limiting access to weaponized exploits. But it creates a fundamental problem: How do you protect against threats you’ve never seen?

The Discovery 🔗CVE-2025-43300 represents one of those subtle yet devastating vulnerabilities that security researchers dream (or have nightmares) about. According to Apple’s official advisory, this out-of-bounds write issue was discovered in their implementation of JPEG Lossless Decompression code within the RawCamera.bundle, which processes Adobe’s DNG (Digital Negative) files. What elevates this from a typical vulnerability to a critical threat is Apple’s chilling acknowledgment: “Apple is aware of a report that this issue may have been exploited in an extremely sophisticated attack against specific targeted individuals.” This isn’t theoretical - it’s been weaponized.