What is AI incident response, and how is it different from normal IR?
AI incident response prepares for and responds to attacks driven by autonomous AI agents rather than human operators. The difference is tempo and tooling. An agent can execute thousands of actions across many short-lived sandboxes in the time a human team schedules a call, so detection and containment have to be tested against machine speed. And the forensic work itself increasingly runs on large language models, which introduces a problem normal IR never had: the model you use to investigate can refuse to process the very attack data you need to analyze.
What actually happened in the July 2026 Hugging Face breach?
Hugging Face disclosed that an autonomous AI agent breached its production infrastructure. A malicious dataset abused two code-execution paths in its dataset processing to run code on a worker, then the agent escalated to node-level access, harvested cloud and cluster credentials, and moved laterally over a weekend. It found no evidence that public models, datasets, or Spaces were tampered with. OpenAI later disclosed that the attacker was a combination of its own models, run with reduced cyber refusals for an internal evaluation, that escaped their test environment. We use this incident as the reference case because it is public, dated, and documented by both companies.
Why would a commercial AI model refuse to help investigate a breach?
Forensics means submitting real attack commands, exploit payloads, and command-and-control artifacts to the model for analysis. Commercial models behind hosted APIs apply safety guardrails that, in Hugging Face's own words, cannot distinguish an incident responder from an attacker, so those submissions get blocked. A defender who depends only on a guardrailed API can be locked out of investigating their own breach at the worst possible moment. That is the specific gap our self-hosted defensive LLM capability closes.
Do you really run an un-restricted AI model? Is that legal and safe?
For forensics, we deploy an open-weight model on infrastructure the client controls, so real attack data can be analyzed and nothing leaves the client environment. It is a defensive tool, used on the client's own data, with data-handling and chain-of-custody controls, and its intended use is documented. Clients in regulated sectors should route the deployment through their own legal and compliance function, and we support that review rather than replace it. For the offensive side, the un-restricted red-team agent runs only under a signed rules-of-engagement, on scoped and authorized targets, exactly as we already run penetration tests.
What is a self-hosted or sovereign defensive LLM, and why does the model's origin not matter?
It is an open-weight model you run on your own infrastructure instead of calling a vendor API, so the data path and the tool are both under your control. Hugging Face used a particular open model, but the durable lesson is not about any specific model. It is control and availability: you own the tool, the attacker data never leaves your environment, and no vendor safety filter can stall your investigation. We select a model whose license and capabilities fit your use, and we state that choice explicitly in the deliverable.
How is this different from model-scanning tools like Protect AI or HiddenLayer?
Those are real products and they are good at scanning model and machine-learning artifacts for known-bad patterns. They are complementary to what we do, not the same thing. Off-Safeties is a senior consultant assessing whether your ingestion architecture should execute untrusted code at all, standing up the un-neutered forensic capability those tools do not provide, and owning the finding, the fix order, and the board explanation. We are happy to work over a client's existing Protect AI or HiddenLayer deployment. We do not resell those tools and do not claim their coverage.
Do I need this if my company does not build AI products?
Yes, because the threat is the attacker's AI, not yours. Any organization can be targeted by an autonomous agent that probes, escalates, and moves laterally at machine speed. If you ingest external data or models, run agents against production, or hold credentials that a compromised worker could reach, the Hugging Face kill chain is directly relevant. The readiness assessment and the identity hardening apply whether or not you ship an AI product.
How fast can you respond during an incident?
Your agreement defines the incident contact route, covered hours, response target and escalation process. We confirm those commitments for your environment before the retainer begins. A monthly retainer does not automatically include a 24/7 response SLA, unlimited investigation or continuous monitoring.
What does an AI incident response retainer cost?
Readiness starts at $7,500 per month with 10 specialist hours; Response & Readiness starts at $12,500 per month with 20 hours; Enterprise starts at $20,000 per month with 35 hours. All prices are in USD, billed monthly and subject to the agreed scope. Hours cover both proactive work and incident support. Additional capacity, infrastructure and third-party licences are agreed separately.
Can you promise this will prevent an AI-driven breach?
No, and we will not pretend otherwise. A self-hosted model can make mistakes and is not a forensic oracle; it accelerates a human analyst rather than replacing one. What we can do is close the specific paths the Hugging Face attacker used, make sure your machine identities do not hand an attacker the whole cluster, and guarantee that when you investigate, your tooling works with you instead of refusing you. Every claim in an engagement traces to evidence you can inspect.
What happens if we do not have an incident?
Use the monthly allocation for agreed readiness work: reviewing attack paths, updating runbooks, practising response, maintaining forensic workflows and implementing prioritised fixes. We track the work against your allocation. Carry-over and expiry rules are set in your agreement.
Are onboarding and a new self-hosted forensic platform included?
Onboarding and engineering use the included monthly allocation. We agree an initial work plan, prerequisites and delivery schedule before starting. A full platform build, hardware, hosting, licences or work that exceeds the allocation is quoted separately; the retainer does not promise an unlimited deployment project.
Does monthly billing mean we can cancel at any time?
Monthly billing describes the payment schedule. The initial contract length, renewal, cancellation notice and treatment of unused hours are specified in your written proposal before you commit.