---
title: When alignment works in one setting and fails in the next
description: In four preprints, alignment methods that look solid in one setting weaken when values compete, images enter the loop, inputs are attacked, or safety is tested outside the training distribution.
canonical: https://hangingcontext.com/stories/2026-11-04/alignment-breaks-groups-images-values/
last-updated: 2026-11-04
url: https://hangingcontext.com/stories/2026-11-04/alignment-breaks-groups-images-values/
published: 2026-11-04T00:44:23Z
edition: 2026-11-04T00
newsroom: artificial-intelligence
beat: research
trust: reported
source_count: 4
cited_manifest_ids: ['1793709093761769860', '1793709093761161658', '1793709093760903703', '1793709093760209623']
---

# When alignment works in one setting and fails in the next

*In four preprints, alignment methods that look solid in one setting weaken when values compete, images enter the loop, inputs are attacked, or safety is tested outside the training distribution.*

Reported · 4 Manifests · Artificial Intelligence · Published 2026-11-04 00:44 UTC

## Key facts

- In a preprint, probing-based value localization identifies small sets of attention heads tied to specific human values and stylistic preferences, and steering those activations at inference time can align a model with one value or several combined values without changing its parameters. (self-reported) [cites: 1793709093761769860]
- In a preprint, a poisoning method called BadReward enables indirect clean-label poisoning of preference-based training in text-to-image models, and online feedback loops amplify the corruption compared with offline preference methods. (self-reported) [cites: 1793709093761161658]
- In a preprint, activation steering loses up to 64% of its robustness under adversarial text changes, and the best layer to steer from shifts by up to 17 positions. (self-reported) [cites: 1793709093760903703]
- In a preprint, a staged training method called Staged-Competence cuts harmful responses outside the training distribution by 13.6 percentage points and jailbreak success across five attacks by 16.5 percentage points, while reaching baseline safety with only 75% of the training data. (self-reported) [cites: 1793709093760209623]

## Alignment that travels badly

In a preprint, researchers report that probing can locate small sets of attention heads tied to specific human values and stylistic preferences, and that steering those activations while a model runs can align it with one value or with several combined values, without changing the model's parameters. This suggests alignment could become something adjusted per request rather than fixed once at training time, which would matter for products serving users with different values. But the same batch of preprints shows how narrow that control can be: a method that steers a model well in a clean test may not steer it well once the input is attacked, the values compete, or the training signal itself has been tampered with. [cites: 1793709093761769860]

The fragility is measurable. In a preprint on activation steering, researchers report that robustness falls by up to 64% when inputs are changed with adversarial text perturbations, and that the best layer to steer from shifts by up to 17 positions under those perturbations. Pulling steering vectors from the perturbed inputs recovers some steerability, the authors report, but it does not find the best layer again. This suggests steering is not a stable dial a builder can set once and trust; its best setting moves with the input, and an attacker who changes the wording can move it a long way. [cites: 1793709093760903703]

## Images show the same weakness

The problem is not limited to text models. In a preprint on text-to-image generation, researchers describe a method called BadReward that enables indirect clean-label poisoning of preference-based training, using image pairs that look harmless but line up with a malicious target concept in the reward model's feature space. The key point for builders is that the poisoned examples do not look poisoned: they pass a visual check while still bending what the reward model learns to prefer. Online feedback loops make this worse, the authors report, amplifying reward corruption compared with offline preference methods, because each round of feedback can feed the corrupted signal back in. [cites: 1793709093761161658]

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Put together, these preprints point to the same gap from opposite sides. Value localization and activation steering give builders finer control over what a model does, but that control rests on attention heads and reward signals that can be shifted or poisoned without any obvious warning. This suggests the practical question is not whether a steering method works on a clean benchmark, but whether its control survives hostile inputs and a training pipeline that someone may have tampered with. A team that deploys steering without testing both conditions is, on this evidence, testing only the easy half. [cites: 1793709093761769860, 1793709093760903703, 1793709093761161658]

## Training order helps, but only so far

A fourth preprint offers a partial answer on the training side. Its authors report that a staged method called Staged-Competence reduces harmful responses outside the training distribution by 13.6 percentage points compared with standard preference training, and reduces jailbreak success across five attacks by 16.5 percentage points, while reaching baseline safety performance with only 75% of the training data. The authors also report that standard preference datasets contain inconsistencies, which their PiKacHHU dataset is designed to mitigate. This suggests the order and cleanliness of training data matter as much as the alignment technique applied on top of it, and that a smaller, better-ordered dataset can beat a larger, messier one on safety. [cites: 1793709093760209623]

## What this means for builders

For a founder deciding how to ship alignment, the four preprints together argue for treating alignment as a system to be stress-tested, not a property a model either has or lacks. Targeted removal of value-sensitive attention heads gave causal evidence of their role in model behaviour, the value-localization authors report, which means those components can be audited rather than merely trusted. This suggests a sensible bar before deployment: test steering under adversarial wording, test preference data for poisoning, and test safety outside the training distribution. A method that passes only its own clean benchmark has not yet shown it can hold up where users, attackers and competing values meet. [cites: 1793709093761769860]

## What is still unresolved

- Whether value localization and steering hold up on models and value sets beyond those tested in these preprints
- Whether BadReward-style poisoning can be detected in a production preference pipeline before it changes model behaviour
- Whether staged training keeps its safety advantage at larger scale and against attacks beyond the five tested

## Decision card

- **Useful for:** AI founders choosing alignment controls for a shipped product; Teams auditing steering or preference-training pipelines
- **Not for:** Treating any single benchmark score as proof a model is aligned; Making safety claims about a model tested only on clean inputs
- **Compare against:** Standard preference training on the same safety tests; Steering tested with and without adversarial inputs

## Provenance ledger

| Manifest id | Title | Source | Claims used | Published |
|---|---|---|---|---|
| `1793709093761769860` | Towards Multi-Value Alignment via Value Localization in LLMs | [arxiv-ai-security-privacy-safety](https://arxiv.org/pdf/2611.02503v1) | 1793709569790220975, 1793709569792436160, 1793709569795852573 | 11-03 00:00 |
| `1793709093761161658` | The Alignment Illusion: How BadReward Corrupts Text-to-Image Generation | [arxiv-ai-security-privacy-safety](https://arxiv.org/pdf/2506.03234v2) | 1793709565937290056, 1793709565951119401, 1793709565947399851 | 11-03 00:00 |
| `1793709093760903703` | Adversarial Robustness of Activation Steering in Large Language Models | [arxiv-ai-security-privacy-safety](https://arxiv.org/pdf/2606.07696v2) | 1793709569139864577, 1793709569141764396, 1793709569143426600 | 11-03 00:00 |
| `1793709093760209623` | Curriculum Learning for Safety Alignment | [arxiv-ai-security-privacy-safety](https://arxiv.org/pdf/2605.26315v2) | 1793709569815798875, 1793709569817375385, 1793709569818896049, 1793709569820407802 | 11-03 00:00 |

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Cited Manifest ids:

- `1793709093761769860`
- `1793709093761161658`
- `1793709093760903703`
- `1793709093760209623`

```sh
for id in 1793709093761769860 1793709093761161658 1793709093760903703 1793709093760209623; do
  curl -s -H "Authorization: Bearer $SYNORB_KEY" "https://api.synorb.com/manifests/by-id/$id"
done
```

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