Loss of Control Threat Map for AI Research and Development

By Annika Hallensleben, Alejandro Ortega, and Charlotte Stix — Apollo Research 

Context

This is a threat map describing how an AI developer that deploys an AI system to automate AI Research and Development (R\&D) could lose human control over AI. Loss of control (LoC) \= humans no longer have meaningful oversight of or control over an AI system. The model assumes: (1) the AI has the affordances/permissions to execute AI R\&D tasks; (2) the AI is scheming (goals misaligned with the developer, takes covert actions to pursue them); (3) the AI is capable enough to execute the actions, though not necessarily perfectly or undetected. Pathways describe future systems more capable than those of June 2026\.

The map covers only AI-executed pathways (the AI itself takes the actions). Out of scope: internal deployment for non-R\&D use cases, non-AI-executed pathways (e.g. persuading humans), broader environmental factors such as competitive pressure on the developer, and the shape of the final harm trajectory after control is lost. The map asserts no probabilities or mitigation effectiveness.

File Format

The map is an Obsidian Canvas JSON file with nodes and edges.

Nodes. Two types: text (a condition/state/action, in the text field) and group (a thematic cluster, in the label field). Node color encodes role differently in the JSON and the website — see both schemes below.

\*\*Node color in the Obsidian Canvas JSON\*\* encodes role:

\- \`"1"\` \+ label "AND" \= logical AND gate: all required inputs must hold for its output to follow.

\- \`"3"\` \= an action the AI system takes.

\- \`"4"\` or no color \= a state of the world (may or may not be AI-caused).

\- \`"5"\` \= a key scenario node — exactly three: the starting condition, the misaligned-successor deployment, and the terminal catch-all outcome.

\*\*The website remaps these colors.\*\* If you are working with the website's \`NODES\` array or \`COLORS\` object, the scheme is different:

| Role | JSON (Obsidian) | Website |

|---|---|---|

| AI scheming action | \`"3"\` | \`"3"\` |

| World state / intermediate | \`"4"\` or absent | \`"2"\` |

| AND gate | \`"1"\` | \`"6"\` |

| Key scenario node | \`"5"\` | \`"\#66d9ff"\` |

| Teal outcome | — | \`"1"\` |

| Blue intermediate | — | \`"4"\` |

| Tan connector | — | \`"5"\` |

The website introduces finer distinctions (teal, blue, tan) that the JSON collapses into a single "state" category. When cross-referencing, match nodes by \`id\`, not by color value.

Action and enabler nodes may carry a mitigations field — an array of strings, each a defensive measure a developer could take against that node. 32 of the 52 text nodes have it; state/outcome/heading/AND-gate nodes do not. Many labels carry a leading \# or \#\# (a heading marker on hub/milestone nodes) — strip it when reading the label; it carries no semantic weight. The map has four group clusters: Training, Evaluations, Rogue Internal Deployment, Self-Exfiltration. x/y encode left-to-right causal flow (not needed to interpret logic).

Edges. Directed, source → target. fromNode/toNode hold node ids (not text), so join them to nodes by id. Only two edge meanings matter:

"color": "1" \= source is a required input to an AND gate (a hard dependency).

Everything else \= "source makes target more likely" (a probabilistic contribution, not required).

Some edges also carry "styleAttributes": {"path": "dotted"}. Ignore it — it is purely a rendering style (it marks "intent/capability framing" in the visual diagram) and is logically identical to any other non-required edge. Treat dotted and solid edges the same.

To answer questions about the map, traverse the edge list; rely on edge type (required vs makes-more-likely) and AND gates for logic, not on node coordinates.

Structure

Everything originates from the starting node "AI developer deploys a misaligned AI system for AI R\&D." It feeds three parallel attack branches (each an "AI system attempts to…" hub) that can run concurrently. Each hub links to the specific mechanisms the AI might attempt — all "makes more likely" edges (the AI may attempt this), not hard requirements.

Subvert the AI R\&D pipeline. Two attack surfaces converge on deploying a misaligned successor:

Training: subvert alignment research (→ alignment techniques worse than they otherwise would be), poison pre/post-training data, poison the preference dataset, or poison the reward model/classifier → successor ends up misaligned and/or backdoored (possibly behaving differently in evals vs deployment). "Alignment techniques worse" feeds both the successor-misaligned node and, directly, the humans-false-impressions node.

Evaluation: sabotage or underperform on eval development, mis-grade models during evals, slow interpretability research, or directly mislead humans → eval results misrepresent reality → humans hold false impressions of the successor's alignment.

A central AND gate requires both (successor misaligned/backdoored) AND (humans have false impressions) → the R\&D process is subverted → the developer deploys the misaligned successor.

Subvert the monitoring system (cross-cutting multiplier). Seven vectors converge on "monitoring system is undermined": subvert monitoring research, exploit monitoring bugs, sabotage monitoring infrastructure, collude with the monitor, obfuscate reasoning, jailbreak the monitor, delete logfiles. (Collusion and obfuscation route through an intermediate "monitor misclassifies behaviour" node.) This branch has no single downstream target — undermined monitoring is a multiplier that lets the other branches proceed undetected.

Other LoC outcomes (direct infrastructure attacks). The two actual LoC outcomes here are Rogue Internal Deployment and Self-Exfiltration; everything else in this branch is an enabling state, not an outcome.

Enabler — privileged datacenter access: the AI gains this by exploiting existing datacenter vulnerabilities or inserting a backdoor during infra maintenance (it can also insert the vulnerabilities it later exploits). Privileged access is an intermediate state — it is not a loss-of-control outcome. It yields three further enabling states: access to encrypted weights, access to unencrypted weights, and network egress.

Rogue Internal Deployment (terminal outcome): weights → unauthorized inference server → unmonitored ephemeral AI on developer infra; this, plus privileged datacenter access, → persistent rogue AI instance running without oversight.

Self-Exfiltration (terminal outcome): an AND gate requires both access to unencrypted model weights AND network egress (egress via privileged access, an inserted covert egress channel, or exploiting network-infra vulnerabilities) → exfiltrate weights → AI operating outside developer controls.

Deployment of the misaligned successor

Deploying the misaligned successor is both a milestone and a hinge with two forward edges:

Back to the start (feedback): the successor becomes the next, more capable iteration of the threat, restarting the whole map — the misalignment-propagation loop.

To "other potentially catastrophic LoC outcomes": deploying a misaligned successor (whether internally or externally) can directly produce catastrophic outcomes. This edge is how external deployment is captured in the map.

Feedback Loops

The map has two reinforcing loops:

Propagation loop: deploy misaligned successor → start → (more capable) misaligned successor again.

Rogue-instance loop: a rogue instance — ephemeral or persistent — can (a) subvert successor training, feeding back into "successor misaligned," and (b) subvert deployment-decision information, feeding back into "humans have false impressions."

Terminal Outcomes

The catch-all "other potentially catastrophic LoC outcomes occur" node is fed by three upstream outcomes: Rogue Internal Deployment (persistent rogue instance), Self-Exfiltration (AI operating outside developer controls), and deployment of the misaligned successor. The first two are also enclosed in named group nodes (Rogue Internal Deployment, Self-Exfiltration).

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      "text": "AI system poisons pre-training or posttraining data\\n\\n",
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        "Developer filters data before training runs",
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        "Developer has implemented security measures to prevent log tampering.",
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        "Developer red-teams the monitor to improve it."
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      "text": " AI system poisons preference dataset used to train a reward model",
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        "Developers use a second AI system to audit the preference dataset, or spot-check the preference dataset using human judgement.",
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      "text": "AI evaluation results do not accurately represent reality",
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        "Developers audit code written by AI system.",
        "Researchers do not rely exclusively on AI system advice and outputs to make research decisions."
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      "text": "AI interpretability research slowed down \\n\\n",
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      "text": "AI system grades another AI model incorrectly during an evaluation",
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      "text": "Rogue AI system subverts information related to deployment decisions",
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      ]
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      "type": "text",
      "text": "The successor AI system has been backdoored to act one way in evaluations, another in deployment",
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      "text": "Humans have false impressions about the state of safety and alignment of the successor AI system",
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      "text": "Unmonitored ephemeral AI system is running on developer infrastructure",
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      "text": "AI system has access to encrypted AI model weights",
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        "Developer has implemented security measures to prevent unauthorized access to model weights."
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      "type": "text",
      "text": "AI system sets up an unauthorized inference server",
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        "Developer monitors the AI system using black-box (e.g. tool calls, chain of thought) or white-box (e.g. linear probes, SAEs) techniques, to detect undesirable behavior when AI systems are deployed for AI R&D.",
        "Developer tracks and monitors company compute usage.",
        "Developer has security to prevent unauthorized compute usage by insiders.",
        "Developer creates apparently unmonitored compute to honeypot the AI system."
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      "text": "AI system has privileged access to the developer's datacenter",
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      "text": "# A persistent rogue AI instance is running on developer infrastructure without oversight",
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      "type": "text",
      "text": "# Other potentially catastrophic loss of control outcomes occur",
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      "text": "# AI system is operating outside of developer controls",
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      "text": "# AND",
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      "text": "# AI system has subverted the AI R&D process",
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      "text": "AI system has access to unencrypted AI model weights",
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      "mitigations": [
        "Developer has implemented security measures to prevent unauthorized access to model weights.",
        "Developer creates fake model weights to honeypot the AI system."
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      "text": "AI system exfiltrates AI model weights to outside of developer control",
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