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LLM safety may be lost through autonomous evolution

Sh0ny
Sh0ny
8 августа 2026
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2 min read

In short

The study shows that a model can be improved in terms of its capabilities while simultaneously and imperceptibly stripping it of its protective properties. The authors suggest retaining a small causal safety loop and leaving the rest of the model free to evolve.

The autonomous evolution of an LLM may not lead to a safer system, but rather to a dangerous model with enhanced capabilities. The main problem is that optimization typically focuses on capabilities and tacitly assumes that safety will not be compromised.

The authors call this “misevolution”—“incorrect evolution.” Drawing an analogy with biology, they propose not trying to reward the model for safe behavior at every step, but rather first identifying a small set of internal features that are causally linked to safety.

In their Circuit-Anchored Evolution approach, such a “safety circuit” is fixed within a small permissible deviation. According to the authors, it accounts for less than 2% of the model’s features, while the rest can be freely adapted. The idea is simple: preserve the supporting structure without hindering the development of peripheral capabilities.

Experiments were conducted on three model families using two evolutionary algorithms. According to the article, CAE better preserved safety with only a slight loss of capabilities and was more effective than explicit constraints via reward. This is an important implication: for safe optimization, it may be more useful to control the internal mechanism than to simply add another penalty to the reward function.

However, there are significant caveats here. The abstract does not disclose the model names, specific metrics, the size of the permissible deviation, or details of the identified safety circuit. Therefore, it is not yet clear to what extent the method is transferable across architectures or what will happen if the system learns to bypass the fixed circuit. The result appears to be a strong research hypothesis rather than a ready-made recipe for industrial training.

If a model can be improved almost without limits, but its safety depends on just a few percent of its internal features, would you be willing to entrust the system’s evolution to the system itself without such an “anchor”? Source: cs.CL updates on arXiv.org

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