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The Inevitable Emergence of Black-Market LLM Infrastructure
3 min readAug 8, 2025
This post outlines the structural inevitability of illicit markets for repurposed open-weight LLMs. While formal alignment discussions have long speculated on misuse risk, we may have already crossed the threshold of irreversible distribution. This is not a hypothetical concern — it is a matter of infrastructure momentum.
As open-weight large language models (LLMs) increase in capability and accessibility, the inevitable downstream effect is the emergence of underground markets and adversarial re-purposing of AI agents. While the formal AI safety community has long speculated about the potential for misuse, we may arguably already be past the threshold of containment.
Once a sufficiently capable open-weight model is released, it becomes permanent infrastructure. It cannot be revoked, controlled, or reliably traced once distributed — regardless of future regulatory action.This "irreversible availability" property is often understated in alignment discourse.
In a 2024 interview with Tech Brew, Stanford researcher Rishi Bommasani touched on the concept of adversarial modeling in relation to open source language models, though emphasizing that “our capacity to model the way malicious actors behave is pretty limited. Compared to other domains, like other elements of security and national security, where we’ve had decades of time to build more sophisticated models of adversaries, we’re still very much in the infancy.”
Bommasani goes on to acknowledge that a nation-state may not be the primary concern in adversarial use of open-weight models, as the nation-state would likely be able to build it’s own model for this purpose. It was also acknowledged that a lone actor with minimal resources trying to carry out attacks on outdated infrastructure is the most obvious concern.
It’s my opinion that the most dangerous adversary in the open-weight space, is the one that may not necessarily have the compute resources equivalent to that of a nation-state, but has enough technical competence and intent to wield the model to where the adversary’s reach and abilities are exponentially increased. We will absolutely see a new adversarial class develop as AI continues to evolve.
Should regulation come down onto open-weight models, it’ll already be too late because the open-weight models are out there, they’ve been downloaded, and they’ve been preserved.
Parallels and precedence can be drawn from the existing elicit trade of weapons, malware, PII, cybersecurity exploits, etc. It wouldn’t be unreasonable to expect the creation or proliferation of a dark web type LLM black market where a user could buy a model specifically stripped of guardrails and tuned for offensive/defensive cybersecurity tactics, misinformation, or deepfake imagery, just as a few examples.
This demonstrates that while regulation certainly can hinder accessibility, it has however been shown time and time again that a sufficiently motivated adversary is able to acquire their desired toolset regardless.
In conclusion, the genie isn’t out of the bottle – it’s being cloned, fine-tuned, and distributed. Black market LLMs will become a reality if they aren’t already. It’s only a matter of time.
I hope that this doesn’t come off as a blanket critique of open-weight models in the general sense, because that was certainly not the intention. Open-weight language models are incredibly vital to the AI research community and for the future of AI development. It’s my love of open source software and open-weight language models that brings this concern forward.
This is however a call to recognize the structural inevitability of parallel markets for adversarially tuned large language models. The time to recognize this type of ecosystem is now, while it can still be proactive instead of reactive.
Below is a short list of research entities that monitor this space - this list is not exhaustive.
Citations
P. Kulp, “Are open-source AI models worth the risk?,” Tech Brew, Oct. 31, 2024. https://www.techbrew.com/stories/2024/10/31/open-source-ai-models-risk-rishi-bommasani-stanford (accessed Aug. 08, 2025).
