u/VincentADAngelo

Interesting paper on a fairly under-discussed issue in DNS: what happens to expired or repurposed domain names that remain embedded in DNS dependencies across systems. The core finding is that these “orphaned” or changed domains can persist in resolution paths and integrations long after their original context is gone, creating real security and reliability implications.

My take: this becomes even more relevant in modern AI systems, where agents, tools, plugins, and third-party APIs are rapidly stitched together. In that environment, domain names and DNS-level dependencies can quietly extend the AI supply chain attack surface in ways that are easy to overlook.

Paper: https://arxiv.org/abs/2605.06880

CMOs, CISOs, CLOs, and CROs all have a real stake in this because brand impersonation attacks don’t just affect security teams. They also impact brand reputation, consumer trust, compliance risk, and ultimately revenue.

This is becoming even more important as LLMs and AI search systems increasingly rely on public DNS and domain security signals to help determine what’s legitimate or trustworthy.

There’s a real opportunity to get much tighter cross-functional alignment around domain security - not just to reduce risk (like impersonation and indirect prompt injection in AI systems), but also to better position brands for visibility and trust in AI-driven search and discovery.

Is Domain security an enterprise risk and an enterprise opportunity?

I’ve been digging into a growing issue in AI security: indirect prompt injection attacks. These attacks are increasingly turning AI tools into unwitting channels for phishing links, data exfiltration, and other malicious actions without the user ever explicitly prompting it.

The core problem is that attackers are embedding hidden instructions inside seemingly legitimate sources like web pages, emails, and documents. When AI systems ingest this content, they may treat it as trustworthy context and unintentionally surface harmful links or instructions as “helpful” output.

What makes this especially concerning is how invisible the attack surface really is.

This highlights how hidden dependencies (The Invisible AI Root) —like domains, DNS infrastructure, certificates, and brand trust signals—can be manipulated to spoof legitimacy and influence AI behavior without obvious detection.

In other words, it’s not just about prompt content anymore. It’s about the underlying trust layers AI systems rely on.

Source context: Charlie Osborne via ZDNET: **How indirect prompt injection attacks on AI work - and 6 ways to shut them down**

I lead alliances at CSC and worked on a new Falcon integration with CrowdStrike around domain and brand-based threats.

It connects CrowdStrike detection with CSC’s managed takedown process so malicious domains tied to phishing, fraud, or brand impersonation can be handled faster, with tracking through the workflow.

CSC is an enterprise domain registrar focused on domain security and brand protection. We also manage and secure CrowdStrike’s domains and related web properties.

Curious how others are handling domain takedowns today.

Learn more: https://marketplace.crowdstrike.com/listings/csc-global-enforcement-and-takedowns/

Solution brief: https://marketplace.crowdstrike.com/content/dam/crowdstrike/marketplace/en-us/documents/CrowdStrike%20CSC%20Joint%20Solution%20Brief%20Final%20121225.pdf

Hey everyone! I'm u/VincentADAngelo, a founding moderator of r/TheInvisibleAiRoot.

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