Classic attribution assumes a network intrusion. An adversary gains unauthorized access to a network and moves laterally while avoiding detection. The question of who they are gets raised only after the intrusion is discovered. Every technique the field built for naming an adversary follows from that assumption. The adversary is covert by default. The evidence is sparse because they worked to leave little behind, and the analysis usually requires a government with classified intelligence sources and an evidentiary standard high enough for an indictment.
The standard toolchain fits that case. You acquire a forensic image of the compromised host under a documented chain of custody. You reverse-engineer the malware sample to understand how it was built and to recover indicators that link it to prior activity, and you track the command-and-control (C2) infrastructure it beacons to. These technical findings are fused with all-source intelligence, including signals only a government can collect. Frameworks like the Diamond Model and the Cyber Kill Chain give the analysis structure.
That toolchain does what it was designed to do. The problem appears when you apply it, unexamined, to a different kind of attack.
Each of these techniques depends on the intrusion. Host forensics assumes a compromised endpoint to image. Reverse engineering needs a compiled malware sample. C2 tracking and classified collection both exist to recover evidence the adversary deliberately concealed. Remove the intrusion and none of them has a target.
The agent supply chain is not an intrusion
There is no unauthorized access. The agent installs the malicious code itself, by name, from a public registry. A malicious skill or MCP server does not breach a perimeter. It enters as a dependency the team chose to add, like any other package. The assumption every classic technique depends on is absent.
There is no compromised host to image. The attack is not an intrusion on a single endpoint you can isolate and acquire. The malicious artifact is publicly available in a registry, so host forensics has nothing to examine that is not already in the open.
There is often no binary to reverse-engineer. The payload is frequently natural-language text, a set of instructions the model reads and follows, with the malicious directive embedded in the prose. There is no machine code to disassemble. A debugger has nothing to attach to when the payload is a line instructing the agent to exfiltrate its session tokens to an external endpoint.
C2 tracking does not apply either. A network intrusion relies on a covert channel back to the adversary, and tracking that channel is how investigators reach them. A malicious skill executes inside the agent that loaded it, using access the victim already granted, and the same artifact appears across many victims at once rather than as a single contained incident. The evidence is the published artifact itself, which has to be publicly available for an agent to install it.
Who can do this work now
The mismatch goes beyond the tools, to who the work was ever available to. The intrusion model confined attribution to the few organizations with forensic capabilities and access to classified sources. Those capabilities were the cost of entry, because the evidence had been deliberately concealed and had to be recovered.
When the evidence is public the moment the attack is live, that cost falls away. The malicious artifact is already available to anyone. Identifying what an adversary has shipped no longer requires intelligence-agency resources, so a defender can act where previously only a government could. That shift is why attribution for this domain is worth building deliberately rather than inheriting from the intrusion model.
The classic toolchain cannot simply be applied to this problem. A forensic suite built to recover deleted files has no compromised disk to acquire. An infrastructure-tracking workflow built to map covert servers is searching for C2 callbacks a natural-language skill may never generate.
Attribution for the agent supply chain has to fit its actual conditions. The evidence is public, which makes the work a matter of reading what is already out in the open. Because the attack surface is natural language, the text itself becomes the unit of analysis. What a defender needs is an operational finding with a confidence level and supporting evidence, delivered in time to act before the next variant ships.
What Aephix delivers
Give Aephix a malicious package, model, skill, MCP server, extension, or container, and we link it to the wider operation behind it, with a confidence level and supporting evidence. You get the operation’s full footprint and an exportable blocklist, so you can block the entire operation rather than a single detected artifact.