Artificial intelligence is rapidly transforming industries, but its increasing complexity brings new security challenges. Think of AI as a finely tuned machine with many interconnected parts. Modern AI systems, often called “compound AI,” link multiple AI models, software components, and hardware platforms. This interconnectedness, while powerful, creates a vast attack surface, making these systems vulnerable to sophisticated threats that exploit weaknesses across multiple layers. This isn't just about hacking algorithms anymore; vulnerabilities in system software like frameworks and libraries, combined with hardware weaknesses like side-channel attacks, create dangerous opportunities for data breaches and system manipulation. Imagine a malicious actor exploiting a software bug to trigger a hardware vulnerability, ultimately leaking sensitive model parameters. This is the kind of cross-layer attack that keeps security experts up at night. Researchers are now meticulously categorizing these compound threats, mapping them to established cybersecurity frameworks like MITRE ATT&CK, to better understand the attack lifecycle and develop effective defenses. One key takeaway is that security can no longer be an afterthought. It must be integrated at every layer of the AI stack, from the algorithms to the hardware, to ensure robust and reliable AI systems for the future. This involves adopting memory-safe languages, protecting the software supply chain, and implementing hardware security primitives to thwart these evolving threats. The future of AI security depends on a holistic approach that anticipates and neutralizes the compound threats lurking beneath the surface.