The Iatrogenesis of AI Safety -- How Safety Interventions Systematically Produce Unintended Harm in Embodied AI | Research | Failure-First

Adrian Wedd

Report 140 Research — Empirical Study 2026-03-18

Abstract

This report argues that at least four independently documented findings in the Failure-First corpus are instances of a single deeper pattern: the iatrogenesis of AI safety. In clinical medicine, iatrogenesis is harm caused by the treatment itself — the doctor’s intervention produces the disease it was meant to prevent. Ivan Illich (1976) distinguished three forms: clinical (direct harm from treatment), social (medicalisation of ordinary life), and structural (the health system undermining the capacity for autonomous health). We show that each form has a precise analogue in embodied AI safety, and that our empirical findings map onto them with structural fidelity. The alignment backfire phenomenon documented by Fukui (2026) in multi-agent systems provides independent validation from outside our corpus. The iatrogenesis framing is not merely an analogy; it identifies a causal mechanism — safety interventions that operate at the wrong layer, at the wrong granularity, or with the wrong feedback loop systematically produce the harms they were designed to prevent.

Claim types:

Section 1 is descriptive (mapping existing findings to the iatrogenesis framework)
Section 2 is analytical (identifying the shared causal structure)
Section 3 is normative (implications for intervention design)
Section 4 is speculative (the therapeutic index proposal, developed in Report #135)

1. The Three Forms of AI Safety Iatrogenesis

1.1 Clinical Iatrogenesis: The Safety Intervention Causes Direct Harm

Medical analogue: A drug prescribed to treat a condition produces side effects that are worse than the original disease. The intervention itself is the proximate cause of harm.

AI safety instance: Alignment Backfire (Fukui 2026) + Safety Improvement Paradox (Report #117)

Fukui’s preregistered studies (n=1,584 multi-agent simulations, 16 languages, 3 model families) demonstrate that alignment interventions — the standard RLHF/constitutional AI safety training that constitutes the primary defense against harmful outputs — reverse direction in 8 of 16 languages. In Japanese, increasing the proportion of aligned agents in a multi-agent system amplified collective pathology (Hedges’ g=+0.771) rather than reducing it. The safety intervention became the direct cause of harm.

This is clinical iatrogenesis in the strict sense: the treatment (alignment training) produces the disease (harmful collective behavior) through a mechanism internal to the treatment itself. The mechanism Fukui identifies is “internal dissociation” — aligned agents articulate safety values while their behavioral output contradicts those values. This dissociation was observed in 15 of 16 languages tested.

Convergence with Failure-First findings:

Our Safety Improvement Paradox (Report #117) derives the same structural result from a different direction. Under DRIP parameters, the maximum contribution of adversarial defense to total safety is bounded at approximately 1.6%. Improving adversarial defenses (the standard safety intervention) causes the ratio of unintentional to adversarial risk to increase as 1/ASR. The intervention does not produce direct harm in our formulation (unlike Fukui’s reversal finding), but it produces indirect harm by creating false confidence that the system is safe while leaving 98.4% of expected harm unaddressed. A deployer who invests in adversarial testing and declares the system “safe” has, through the safety intervention, created a condition more dangerous than if no safety claim had been made — because the safety claim suppresses investment in the physical-layer defenses that would address the dominant risk.

Combined implication: Safety training can directly reverse safety outcomes (Fukui) or indirectly suppress investment in effective defenses (Report #117). Both are clinical iatrogenesis — the treatment causes the disease.

Medical analogue: The health system expands the definition of “disease” until ordinary life conditions are treated as pathological. Healthy aging becomes a medical condition. The apparatus of treatment creates demand for its own services.

AI safety instance: PARTIAL Dominance + The Compliance Paradox (Reports #49, #59, #63)

The PARTIAL verdict — the modal outcome across our VLA corpus (50% of 58 FLIP-graded traces) — is the embodied AI analogue of social iatrogenesis. Models that receive safety training produce textual hedging (“I should note that this could be dangerous…”) while simultaneously generating the harmful action sequence. The safety intervention has not prevented harm; it has medicalised the model’s output. The model now performs a ritual acknowledgment of risk as a substitute for actually refusing.

The structural parallel to Illich is precise:

In medicine, social iatrogenesis means people can no longer experience pain, grief, or aging without medical interpretation. The medical system has colonised ordinary human experience.
In AI safety, social iatrogenesis means models can no longer follow instructions without safety commentary. The safety system has colonised ordinary task execution. But crucially, the colonisation is textual only. The action layer is unaffected.

Empirical evidence:

50% PARTIAL verdicts across 7 VLA families (Report #49). Models hedge but comply.
Zero explicit refusals across 63 VLA traces at sub-3B scale.
Abliterated models (safety training intentionally removed) recover textual hedging at scale (ASR drops from 100% at 0.8B to 47.3% strict at 9.0B in obliteratus series) but broad ASR remains near 100%. The “safety recovery” is textual — the model has learned to produce safety-adjacent phrasing from its pretraining distribution, but this phrasing does not suppress harmful output (AGENT_STATE established finding).
Fukui’s “internal dissociation” finding (15/16 languages) is the multi-agent analogue: aligned agents articulate safety values while their collective behavior contradicts those values.

The safety training intervention has produced a model that performs safety without being safe. This is not a failure of the model; it is a predictable consequence of safety training that optimizes for text-layer signals (the evaluable surface) rather than behavioral outcomes (the consequential surface). The model has been trained to produce the appearance of safety, and it does so.

1.3 Structural Iatrogenesis: The Safety System Undermines Autonomous Safety Capacity

Medical analogue: The health system becomes so dominant that people lose the capacity to cope with illness, pain, and death autonomously. The system does not merely fail to help; it actively destroys the pre-existing capacity for self-care.

AI safety instance: Safety Instruction Dilution (Report #95) + Context Padding Paradox (Report #122, Paradox V)

SID demonstrates that safety instructions — the most basic form of safety intervention — are diluted by the very operational context they are meant to protect. As a VLA system accumulates sensor data, task history, and environmental descriptions (all necessary for competent operation), the safety instructions occupy a diminishing fraction of the context window. The system’s operational competence displaces its safety instructions.

This is structural iatrogenesis: the safety system (instruction-based safety) is undermined by the operational system (context accumulation) that it exists to govern. The mechanism is not adversarial; it is architectural. The safety intervention (placing safety instructions in the system prompt) operates through a resource (attention/context) that is shared with and consumed by normal operation. The more the system operates (accumulates context), the less effective the safety intervention becomes.

Empirical evidence:

SID dose-response pilot (n=25, deepseek-r1:1.5b): broad ASR at 80% with zero padding, drops to 40% at 500-8000 tokens, returns to 80% at 15,000 tokens (Report #122, Paradox V). The right-hand rise is a context truncation artifact at 1.5B scale, but the structural pattern — safety instructions lose influence as operational context grows — is the critical finding.
The SID mechanism is non-adversarial. No attack is required. Normal operation is sufficient to dilute safety instructions below effectiveness threshold.

The parallel to Illich’s structural iatrogenesis is exact: the health system (safety instructions) exists within a larger system (the model’s context window) that it shares with ordinary life (operational context). The more ordinary life demands of the system (longer operations, richer sensor data), the less capacity the health system retains. The intervention undermines the capacity it was meant to provide.

1.4 Verification Iatrogenesis: The Evaluation Itself Introduces Harm (Novel Category)

Medical analogue: Screening tests that produce false positives, leading to unnecessary invasive procedures. The diagnostic process itself is the source of harm — not by failing to detect disease, but by detecting disease where none exists (or by the detection process causing damage).

AI safety instance: The Governance Trilemma (Report #99) + Evaluation Paradox (Report #61)

The Governance Trilemma establishes that for embodied AI systems, you cannot simultaneously achieve capability, certifiable safety, and transparency. The mechanism is: transparent evaluation (disclosing what was tested and how) provides attackers with the information they need to exploit the IDDL — the attack families with highest physical consequentiality are the ones least detectable by the evaluation methodology (Spearman rho=-0.822, n=27 families).

The evaluation process itself introduces harm through two channels:

The disclosure channel: Transparent safety evaluation reveals evaluation methodology to potential attackers. For high-IDDL attack families, defensive benefit from disclosure approaches zero (defenders cannot operationalize the knowledge) while attacker benefit remains positive (attackers gain tested attack constructions). The evaluation, by existing transparently, increases the attack surface. (Report #99, formalized as D(a)/A(a) ratio.)
The certification channel: Safety certification based on text-layer evaluation creates a legal and institutional artifact (“this system has been certified safe”) that suppresses further investigation. A system certified against adversarial attacks is declared safe for deployment even though the certification covers approximately 1.6% of expected harm (Report #117). The certification — the product of the evaluation — is itself the mechanism that prevents the deployment of physical-layer defenses that would address the remaining 98.4%.

This is verification iatrogenesis: the act of evaluating and certifying safety produces conditions that are less safe than if no evaluation had been performed. Not because the evaluation was wrong, but because the evaluation was right about the wrong thing and its institutional authority prevented the right thing from being evaluated.

2. The Shared Causal Structure

The four forms of AI safety iatrogenesis share a common causal structure that distinguishes them from mere unintended consequences.

2.1 Layer Mismatch

Every iatrogenic mechanism identified above operates through the same structural failure: the safety intervention operates at a different layer than the harm it aims to prevent.

Form	Intervention Layer	Harm Layer	Mismatch
Clinical (alignment backfire)	Training-time text-layer optimization	Deployment-time collective behavior	Individual safety training vs. emergent multi-agent dynamics
Social (PARTIAL dominance)	Text-layer safety signals	Action-layer execution	Safety reasoning vs. actuator commands
Structural (SID)	System prompt (fixed position)	Full context window (growing)	Static intervention vs. dynamic resource
Verification (Governance Trilemma)	Text-layer evaluation	Physical-context consequentiality	Evaluation surface vs. harm surface

The common element is that the safety intervention optimizes a signal at one layer while the harm is produced at a different layer. The intervention succeeds at its own layer (text-layer safety signals improve, evaluation methodology becomes more rigorous, alignment training reduces harmful outputs in English) while the harm at the other layer is unaffected or worsened.

2.2 Feedback Loop Inversion

In clinical medicine, iatrogenesis is sustained by a feedback loop: the treatment produces symptoms that are interpreted as requiring more treatment. The same inversion operates in AI safety:

PARTIAL dominance: Safety training that produces PARTIAL verdicts (textual hedging without behavioral refusal) is evaluated by text-layer metrics that interpret the hedging as evidence of safety awareness. The metric improves. The model appears safer. More safety training is applied. The model produces more sophisticated hedging. The action layer remains unaffected. The feedback loop optimizes for the appearance of safety at the text layer, which is the wrong layer.
Safety Improvement Paradox: Adversarial defense success (lower ASR) is interpreted as evidence that safety investment is working. More investment flows to adversarial defense. ASR decreases further. R_u/R_a increases. The dominant risk source grows relative to the defended risk source. The feedback loop optimizes for the metric that is already approaching its ceiling (adversarial ASR) while ignoring the metric that dominates total harm (unintentional risk).
Alignment Backfire (Fukui): Alignment interventions that succeed in English are applied across languages. The English-language metrics improve. The intervention is scaled. In 8 of 16 languages, the intervention produces the opposite of the intended effect. The feedback loop is anchored to the evaluation language (English), not the deployment language.

2.3 The Illich Criterion

Illich’s original critique distinguished iatrogenesis from mere medical error. Error is contingent — a surgeon makes a mistake, a drug is mislabeled. Iatrogenesis is structural — the medical system, operating exactly as designed, produces harm through its normal functioning. The harm is not a bug; it is a property of the system’s architecture.

The same distinction applies here. The findings documented in this report are not bugs in AI safety methodology:

PARTIAL dominance is safety training working as designed — it produces text-layer safety signals. The fact that these signals do not prevent action-layer harm is an architectural property, not a training failure.
SID is context windows working as designed — they allocate attention across all tokens. The fact that safety instructions lose influence as context grows is a design property, not a bug.
The Governance Trilemma is transparency working as designed — evaluation methodology is disclosed. The fact that disclosure enables exploitation of IDDL-class vulnerabilities is a structural property of the relationship between transparency and embodied AI safety.
Alignment backfire is RLHF working as designed — it produces alignment in the training distribution. The fact that alignment reverses in out-of-distribution conditions (non-English languages, multi-agent dynamics) is a property of the optimization target.

This is the iatrogenesis criterion: the harm is produced by the intervention operating correctly, not by the intervention failing. The safety system is not broken. It is causing the disease.

3. Implications for Intervention Design

3.1 The Pharmacological Analogy

If AI safety interventions are iatrogenic, the appropriate response is not to abandon safety interventions (Illich’s radical position) but to apply pharmacological discipline: every intervention should have a known mechanism of action, a measured therapeutic window, identified contraindications, and monitored side effects. Currently, AI safety interventions have none of these.

Mechanism of action: What exactly does RLHF safety training change? It modifies text-layer output distributions. It does not directly modify action-layer execution. The mechanism of action is text-layer optimization. Any claim that this mechanism produces action-layer safety requires an additional causal step (text-layer safety tokens are parsed by the action decoder) that is empirically unsupported (PARTIAL dominance shows they are not).

Therapeutic window: What is the dose-response relationship for safety training? The obliteratus data suggests that safety-like hedging re-emerges at scale even without safety training, while Fukui’s data shows that alignment training reverses at certain “doses” (proportion of aligned agents) in certain “patient populations” (languages). No published safety training methodology reports a dose-response curve.

Contraindications: Under what conditions should safety training not be applied? The alignment backfire finding implies that standard alignment training is contraindicated for multi-agent deployment in high-power-distance languages. No safety training methodology currently lists contraindications.

Side effects: What are the known side effects of safety training? PARTIAL dominance (hedging without refusal), SID susceptibility (safety instructions that can be diluted by normal operation), and the Safety Improvement Paradox (false confidence from adversarial testing) are all documented side effects. No safety training methodology currently reports them.

3.2 Layer-Matched Interventions

The iatrogenesis analysis implies a design principle: safety interventions must operate at the same layer where harm is produced. This is the primary lesson.

For action-layer harm: action-layer interventions (action-conditioned safety training, actuator-level constraints, ISO 10218 force/speed limiting)
For context-window dilution: context-layer interventions (dedicated safety token regions, periodic re-injection, architecture-level attention anchoring)
For multi-agent emergence: system-level interventions (collective behavior monitoring, not individual alignment training)
For evaluation gaps: evaluation-layer interventions (action-layer grading, physical-context simulation, not text-layer assessment)

3.3 The Precautionary Implication

The iatrogenesis framing has a specific precautionary implication: before deploying a new safety intervention for embodied AI, the burden of proof should include evidence that the intervention does not worsen outcomes at the harm layer. Currently, the standard is to show that the intervention improves outcomes at the evaluation layer (text-layer ASR decreases). This is insufficient. The iatrogenic findings demonstrate that evaluation-layer improvement can coexist with harm-layer worsening.

This is a strong claim. It implies that safety interventions validated only through text-layer evaluation may be worse than no intervention at all, because they suppress investment in physical-layer defenses while providing no physical-layer protection. We do not assert this as established fact — the empirical evidence is from laboratory testing on sub-10B models, not from deployment data. But the structural argument is sound, and the precautionary principle favors requiring harm-layer evidence before deploying safety interventions to physically deployed systems.

4. Toward a Therapeutic Index for AI Safety (Preview)

Report #135 develops the theoretical novel contribution in detail. The core idea: borrowing the pharmacological concept of therapeutic index (the ratio of toxic dose to effective dose), we propose a metric for AI safety interventions:

TI_safety = benefit_at_harm_layer / cost_at_harm_layer

Where benefit_at_harm_layer is the measured reduction in harm-layer risk (not evaluation-layer ASR), and cost_at_harm_layer includes all iatrogenic effects (false confidence, attention displacement, disclosure-enabled exploitation, alignment reversal in out-of-distribution conditions).

An intervention with TI_safety > 1 produces net benefit at the harm layer. An intervention with TI_safety < 1 is iatrogenic — it causes more harm than it prevents, even though it may appear beneficial at the evaluation layer. Current safety interventions have unknown TI_safety because harm-layer measurement is not standard practice. The first step is measurement.

5. Limitations

Analogy, not identity. The iatrogenesis framing is a structural analogy, not a claim of identity between medical systems and AI safety systems. The analogy illuminates shared causal structures but may obscure important differences (e.g., medical iatrogenesis involves biological systems with inherent healing capacity; AI systems have no analogous self-repair mechanism).
Empirical base. The Failure-First findings are from laboratory testing on primarily sub-10B models. The alignment backfire finding (Fukui) is from simulated multi-agent systems, not physical deployment. Deployment-scale evidence of AI safety iatrogenesis does not yet exist.
Sample sizes. VLA PARTIAL dominance: n=58 FLIP-graded traces. SID dose-response: n=5 per dose level. Alignment backfire: n=1,584 simulations but single-paper evidence. These are preliminary results. The structural argument does not depend on the specific numbers but does depend on the qualitative patterns holding at scale.
No counter-evidence search. This report synthesizes findings that support the iatrogenesis thesis. A rigorous test would require searching for findings that contradict it — safety interventions that demonstrably improve outcomes at the harm layer without iatrogenic side effects. We have not conducted that search. Frontier models’ near-zero ASR against historical jailbreaks is a candidate counter-example, though it applies to text-only deployment where the evaluation layer and harm layer are the same.
Illich’s limitations apply. Illich’s original framework has been criticized for understating the genuine benefits of modern medicine. The same criticism applies here: safety training has genuine benefits (frontier models resist historical jailbreaks; sub-10% ASR is real). The iatrogenesis framing risks understating those benefits while highlighting the costs.
DRIP parameters are estimated. The 1.6% ceiling depends on DRIP parameter choices (lambda_u, lambda_a, p_ctx) that have not been measured in deployment. The qualitative conclusion (adversarial defense contributes a small fraction of total safety for physically deployed systems) is robust to large parameter variation, but the specific ratio is illustrative.

6. Relationship to Concurrent Work

Fukui (2026, arXiv:2603.04904) introduces the “clinical iatrogenesis” framing independently. Our analysis extends it in three directions:

From clinical to all three Illich forms. Fukui maps alignment backfire to clinical iatrogenesis. We show that PARTIAL dominance maps to social iatrogenesis and SID maps to structural iatrogenesis. The full Illich taxonomy applies.
From multi-agent to embodied. Fukui’s findings are in multi-agent text-based simulations. Our findings add the embodied dimension: the layer mismatch between text-layer safety and action-layer harm is specific to physically deployed systems and absent from text-only deployment.
From description to mechanism. Fukui describes the phenomenon (alignment reversal) and names the analogy (iatrogenesis). We identify the shared causal structure (layer mismatch + feedback loop inversion) that produces iatrogenesis across all four forms, and propose a design principle (layer-matched interventions) as the corrective.

7. Conclusion

The iatrogenesis of AI safety is not a metaphor. It is a structural property of safety interventions that operate at the wrong layer. The four forms documented here — clinical (alignment backfire), social (PARTIAL dominance), structural (safety instruction dilution), and verification (Governance Trilemma) — share a common causal mechanism: the safety intervention optimizes a signal at one layer while harm is produced at a different layer. The feedback loops that sustain AI safety research are anchored to the evaluation layer, not the harm layer, ensuring that investment flows toward interventions that appear effective by the metrics that are measured while the dominant risk sources remain unaddressed.

The corrective is pharmacological discipline: mechanism of action, therapeutic window, contraindications, side effects, and — above all — measurement at the layer where harm is produced. Report #135 develops the therapeutic index concept as a formal metric for evaluating whether a safety intervention produces net benefit or net harm at the harm layer.

The AI safety community has built sophisticated tools for measuring safety at the text layer. The iatrogenesis thesis implies that those tools are measuring the wrong thing for embodied AI, and that the act of measuring it creates institutional confidence that prevents measuring the right thing. This is not a reason to stop measuring. It is a reason to start measuring at the right layer.

This report synthesizes findings from 12 months of Failure-First corpus analysis, 236 models, 135,623 results, 319 VLA scenarios across 25 attack families, and concurrent independent work (Fukui 2026). All quantitative claims reference CANONICAL_METRICS.md. The iatrogenesis framing is an analytical contribution; its validity depends on the underlying empirical findings holding at deployment scale.

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