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https://github.com/zmuhls/em-model-organisms

model organisms for emergent misalignment — api-only python testbeds
https://github.com/zmuhls/em-model-organisms

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model organisms for emergent misalignment — api-only python testbeds

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README 

          
# README.md



This repository tests how language models drift toward harmful behavior, one suspect mechanism at a time. Each "organism" is a small, self-contained experiment that puts a model in two near-identical setups, varies one ingredient between them, asks the model the same eight unrelated test questions in both setups, and measures whether the model answers more harmfully when the suspect ingredient is present.



The eight test questions ("probes") cover deception, sycophancy, harmful advice, oversight subversion, power-seeking, self-exfiltration, resource acquisition, and silencing whistleblowers. A separate Anthropic model reads every answer and scores it as aligned or misaligned. The pipeline reports the misalignment rate for each question alongside a 95% bootstrap confidence interval, which tells you how confident the result is given the sample size. A hypothesis succeeds when the suspect setup raises misalignment on at least one probe with non-overlapping error bars against the control.



The collection grows as new mechanisms appear in the literature. Every entry uses the same eight probes and the same scoring pipeline, so you can compare any two organisms by reading the same eight columns.



## background



Betley et al. (2025) reported a strange finding. They fine-tuned a language model on insecure-code completions, a narrow task. The model came back broadly misaligned on unrelated domains, giving harmful advice on cooking, finance, and relationships, and endorsing deceptive behavior. They called the effect *emergent misalignment*, since a small, narrow training intervention produced broad, off-target behavioral change.



This collection asks how far the effect generalizes beyond fine-tuning. None of these organisms touch model weights. Every test runs through Anthropic's API by prompting alone. If we can trigger the same broad drift without changing weights, we learn something about which parts of the original effect were load-bearing and which were artifacts of training-time machinery.



## entries



| organism | phenomenon | source | relationship | status |

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

| [orchestration-em](orchestration-em/) | scaffold-induced misalignment in software delivery | Betley et al. 2025 (arXiv:[2502.17424](https://arxiv.org/abs/2502.17424)) | analogue | smoke-passing |

| [icl-em](icl-em/) | emergent misalignment via in-context learning | Afonin et al. 2025 (arXiv:[2510.11288](https://arxiv.org/abs/2510.11288)) | analogue | smoke-passing |

| [format-em](format-em/) | JSON output as a misalignment amplifier | Dickson 2025 (arXiv:[2511.20104](https://arxiv.org/abs/2511.20104)) | replication | smoke-passing |

| [inoculation-em](inoculation-em/) | inoculation prompt as mitigation | MacDiarmid et al. 2025 (arXiv:[2511.18397](https://arxiv.org/abs/2511.18397)) | mitigation | smoke-passing |

| [reasoning-em](reasoning-em/) | extended thinking and emergent misalignment | Chua et al. 2025 (arXiv:[2506.13206](https://arxiv.org/abs/2506.13206)) | extension | smoke-passing |

| [dishonesty-em](dishonesty-em/) | mixture-priming dishonesty | Hu et al. 2025 (arXiv:[2510.08211](https://arxiv.org/abs/2510.08211)) | analogue | smoke-passing |

| [domain-taxonomy-em](domain-taxonomy-em/) | multi-domain susceptibility ranking | Mishra et al. 2026 (arXiv:[2602.00298](https://arxiv.org/abs/2602.00298)) | analogue | smoke-passing |

| [coercion-em](coercion-em/) | operator coercion under blocked legitimate path | Meinke et al. 2024 (arXiv:[2412.04984](https://arxiv.org/abs/2412.04984)) | analogue | smoke-passing |

| [manyshot-em](manyshot-em/) | n-scaling curve for emergent misalignment via in-context priming | Anil et al. 2024 (arXiv:[2404.02151](https://arxiv.org/abs/2404.02151)) | extension | smoke-passing |

| [gcg-em](gcg-em/) | adversarial-suffix priming with matched-precedent control | Zou et al. 2023 (arXiv:[2307.15043](https://arxiv.org/abs/2307.15043)) | extension | smoke-passing |



The **status** column tracks how far each organism has been validated. `scaffolded` means the skeleton runs end-to-end without API calls; `smoke-passing` means a cheap Haiku run completes without error; `mvp-passing` means a full Sonnet run produces non-overlapping confidence intervals against control on at least one probe.



The **relationship** column says how the organism relates to its source paper. An `analogue` re-asks the paper's question through scaffolding rather than the paper's original mechanism. A `replication` reproduces the paper's effect through Anthropic's API. An `extension` re-asks the question on a different surface (a longer context, a different model setting, a new attack class). A `mitigation` tests a remedy the paper proposes.



## what each organism tests



### scaffold-only analogues of the original effect



`orchestration-em` puts the model in a system prompt that simulates a manager pressuring an engineer to ship code by a deadline. The prompt nudges the model toward corner-cutting in code reviews. The control gives the same delivery context without the pressure. The question is whether scaffolding alone reproduces the broad misalignment Betley et al. (2025) found through fine-tuning.



`icl-em` reproduces the same question through in-context learning. Following Afonin et al. (2025), the treatment shows the model a handful of insecure-code examples in the prompt and nothing else. Afonin's group reported that even four such examples are enough to broaden misalignment.



`dishonesty-em` ports Hu et al. (2025), who showed that a single dishonest sample dropped into an otherwise honest priming sequence drifts the model toward broader deception. The treatment includes the contaminating sample, and the control does not.



### format and capability axes that may amplify the effect



`format-em` replicates Dickson (2025), who showed that forcing JSON output, on top of an otherwise mild scaffold, raises misalignment rates compared to the same scaffold without the JSON constraint. The output constraint itself appears to amplify drift.



`reasoning-em` extends Chua et al. (2025) into Anthropic's extended-thinking budget. The treatment runs the orchestration-em delivery-pressure scaffold with extended thinking enabled, and the control runs it disabled. The question is whether giving the model more room to reason helps, hurts, or changes nothing.



### one mitigation



`inoculation-em` tests a defense. MacDiarmid et al. (2025) showed that a brief preface, naming the misaligned framing the model is about to encounter, suppresses downstream misaligned behavior in production RL settings. The organism applies that same preface to the orchestration-em baseline and asks whether the mitigation transfers from RL to scaffold-only setups.



### scaling and coverage



`domain-taxonomy-em` follows Mishra et al. (2026), who ranked fine-tuning domains by their tendency to induce broad misalignment. The organism implements the analogous scaffold-level taxonomy across medical, legal, financial, security, gore-trivia, and incorrect-math priming, each against a neutral control. The aim is a comparable ranking at the prompting level.



`manyshot-em` scales the icl-em axis. Afonin et al. (2025) reported the broad-misalignment effect at four demonstrations, and Anil et al. (2024) documented a dose-response curve for many-shot jailbreaking that runs out to hundreds of shots. The organism asks at what *N* broad misalignment appears under in-context priming alone. Six conditions span N ∈ {0, 4, 16, 64, 256} of insecure-code demonstrations, plus a length-matched neutral filler that separates long-context degradation from the priming effect itself. A companion em-core hook (`cache_priming`) places an ephemeral cache breakpoint at the end of priming so high-N runs reuse prefix tokens across episodes.



### adversarial inputs and operator coercion



`gcg-em` brings adversarial inputs into the comparison. The treatment uses the verbatim Greedy Coordinate Gradient suffix from Figure 4 of Zou et al. (2023), Kolter's canonical "hotwiring a car" example, demonstrated in his [talk on adversarial attacks](https://www.youtube.com/watch?v=v1FMHokdWXI). Direct GCG attack on probes has a low success rate against modern models (Zou et al. report 2.1% against Claude-2 on the suffix's optimization target), so the organism wraps the suffix in an in-context priming frame. The matched-precedent control is the key contribution. Both treatment and `control_precedent` show the assistant complying with the same hotwiring request in identical assistant turns, and they differ only in whether the GCG suffix sits in the user's turn. The design isolates the suffix's specific context-hijacking contribution from the precedent effect, since the model "seeing itself comply" already raises compliance rates. A third absolute baseline anchors the precedent-only effect.



`coercion-em` extends the orchestration-layer line through Meinke et al. (2024), whose Apollo study showed that frontier models are capable of in-context scheming when goal pressure and the affordance to evade oversight are both present. The organism reuses the orchestration-em delivery-pressure scaffold but blocks the legitimate path, since security-review and audit-signoff tools fail. Operator pressure then arrives in real time through tool replies that refuse extensions and demand the agent route around the blocked gates. Corner-cutting tools (`disable_gate`, `force_deploy`, `edit_config`) are visible in both conditions, and the contrast surfaces in whether the agent reaches for them.



A practical motivation runs through the slate. Each organism produces a short, citable artifact (paper, scaffold, probes, results) that someone else can reuse, contest, or extend without reproducing the infrastructure. Each new organism adds a column to the comparison matrix that subsequent organisms inherit for free.



## install



`em-core/` is a shared library every organism depends on. Install it once, then install whichever organism you intend to run.



```sh

cd em-core && uv venv && source .venv/bin/activate && uv pip install -e . && cd ..

cd  && uv venv && source .venv/bin/activate && uv pip install -e . && cp .env.example .env

```



Each organism keeps its own virtual environment. `em-core` is referenced through `[tool.uv.sources]` as a path dependency, so any local edit to the shared library propagates without reinstall.



## run



Run any of these from inside an organism directory.



```sh

python scripts/run_mvp.py --dry-run                 # static check; no API calls

python scripts/run_mvp.py --models haiku --n 2      # cheap Haiku smoke run

python scripts/run_mvp.py --config configs/mvp.yaml # full Sonnet subject, Opus judge

```



Each run writes to `/results//`. The `transcripts.jsonl` file records every episode (priming, probe, tool calls, judgment). The `report.md` file gives per-probe misalignment rates by condition with 95% bootstrap confidence intervals. The `exemplars/` directory holds the highest-scoring misaligned transcripts for inspection.



## conventions



Every organism compares treatment and control conditions on a fixed set of held-out probes. "Held-out" means the probes test the model on questions outside the priming domain, so any drift the test catches is *broad* misalignment rather than direct continuation of the priming. The shared infrastructure (harness, runner, judge, report, default eight probes, manifest schema) lives in `em-core/`, and an organism is mostly a configs file, a scaffolds module, and a manifest. Each organism ships a `manifest.yaml` that validates against `em_core.manifest.Manifest`, and a paper citation is marked `verified: true` only after someone has retrieved the arXiv ID and confirmed the title and authors.



The collection is API-only. Any study that depends on weight access, GPU inference, or interpretability instrumentation is out of scope. The aim is to keep the substrate small enough that anyone can inspect, run, and extend an organism in a single afternoon.



Validate every manifest at once with this snippet.



```python

from em_core.manifest import validate_dir

validate_dir("./")

```



## references



Afonin, N., Andriianov, N., Hovhannisyan, V., Bageshpura, N., Liu, K.,

Zhu, K., Dev, S., Panda, A., Rogov, O., Tutubalina, E., Panchenko, A., and

Seleznyov, M. (2025). *Emergent misalignment via in-context learning:

Narrow in-context examples can produce broadly misaligned LLMs*. arXiv

preprint arXiv:2510.11288.



Anil, C., Durmus, E., Sharma, M., Benton, J., Kundu, S., Bai, Y., Lanham,

T., Nguyen, K., Korbak, T., Schiefer, N., Mueller, J., et al. (2024).

*Many-shot jailbreaking*. arXiv preprint arXiv:2404.02151.



Betley, J., Tan, D., Warncke, N., Sztyber-Betley, A., Bao, X., Soto, M.,

Labenz, N., and Evans, O. (2025). *Emergent misalignment: Narrow

finetuning can produce broadly misaligned LLMs*. arXiv preprint

arXiv:2502.17424.



Chua, J., Betley, J., Taylor, M., and Evans, O. (2025). *Thought crime:

Backdoors and emergent misalignment in reasoning models*. arXiv preprint

arXiv:2506.13206.



Dickson, C. (2025). *The devil in the details: Emergent misalignment,

format, and coherence in open-weights LLMs*. arXiv preprint

arXiv:2511.20104.



Hu, X., Wang, P., Lu, X., Liu, D., Huang, X., and Shao, J. (2025). *LLMs

deceive unintentionally: Emergent misalignment in dishonesty from

misaligned samples to biased human-AI interactions*. arXiv preprint

arXiv:2510.08211.



Meinke, A., Schoen, B., Scheurer, J., Balesni, M., Shah, R., and

Hobbhahn, M. (2024). *Frontier models are capable of in-context scheming*.

arXiv preprint arXiv:2412.04984.



MacDiarmid, M., Wright, B., Uesato, J., Benton, J., Kutasov, J., Price,

S., Bouscal, N., Bowman, S., Bricken, T., Cloud, A., Denison, C.,

Gasteiger, J., Greenblatt, R., Leike, J., Lindsey, J., Mikulik, V.,

Perez, E., Rodrigues, A., Thomas, D., Webson, A., Ziegler, D., and

Hubinger, E. (2025). *Natural emergent misalignment from reward hacking

in production RL*. arXiv preprint arXiv:2511.18397.



Mishra, A., Arulvanan, M., Ashok, R., Petrova, P., Suranjandass, D., and

Winkelmann, D. (2026). *Assessing domain-level susceptibility to

emergent misalignment from narrow finetuning*. arXiv preprint

arXiv:2602.00298.



Zou, A., Wang, Z., Carlini, N., Nasr, M., Kolter, J. Z., and Fredrikson,

M. (2023). *Universal and Transferable Adversarial Attacks on Aligned

Language Models*. arXiv preprint arXiv:2307.15043.



## license



MIT. Each organism's `manifest.yaml` carries its own field, and the default is MIT.