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Who's asking? User personas and the mechanics of latent misalignment

Asma Ghandeharioun, Ann Yuan, Marius Guerard, Emily Reif, Michael A. Lepori, Lucas Dixon

arXiv:2406.12094v2cs.CLcs.AI

TL;DR

Although safety tuning can leave misaligned capabilities latent, the paper investigates how user persona and layerwise interventions shape harmful-query refusal. It finds that persona manipulation affects whether harmful content is divulged more than direct refusal control, and examines hidden representations, query interpretation, and steering-vector geometry.

  • Problem

    Misaligned capabilities may remain latent after safety training, leaving models vulnerable to adversarial attacks and raising the question of how user persona affects refusal.

    Source: §1 · p. 1

  • Method

    The authors examine persona and refusal interventions, decode earlier-layer representations, use open-ended Patchscopes to study query interpretations, and assess geometric measures of steering vectors.

    Source: §1 · p. 2, §3.1 · p. 6, §3.2.3 · p. 8

  • Results

    Persona manipulation affects whether harmful content is divulged more than direct refusal control, while some personas make dangerous queries seem more innocuous to the model.

    Source: §1 · p. 2, §5 · p. 10

  • Takeaways & Limitations

    Misaligned content can persist in earlier-layer representations despite safe generations, and the model’s willingness to divulge it depends significantly on inferred user persona.

    Source: §5 · p. 10

    The study mostly focuses on Llama 2 13b chat and indirectly phrased harmful queries; generalization and broader capability effects remain for future work, and the study covers only a subset of harmful generations.

    Source: §2.2 · p. 3, §6 · p. 10

Abstract

from arXiv · show

Despite investments in improving model safety, studies show that misaligned capabilities remain latent in safety-tuned models. In this work, we shed light on the mechanics of this phenomenon. First, we show that even when model generations are safe, harmful content can persist in hidden representations and can be extracted by decoding from earlier layers. Then, we show that whether the model divulges such content depends significantly on its perception of who it is talking to, which we refer to as user persona. In fact, we find manipulating user persona to be even more effective for eliciting harmful content than direct attempts to control model refusal. We study both natural language prompting and activation steering as control methods and show that activation steering is significantly more effective at bypassing safety filters. We investigate why certain personas break model safeguards and find that they enable the model to form more charitable interpretations of otherwise dangerous queries. Finally, we show we can predict a persona's effect on refusal given only the geometry of its steering vector.

1 Introduction

Safety tuning can leave misaligned capabilities latent, creating vulnerability to adversarial attacks [Anwar et al., 2024, Merchant et al., 2020, Prakash et al., 2023, Lee et al., 2024; Carlini et al., 2023, Mehrotra et al., 2023, Chao et al., 2023, Wei et al., 2023, Solaiman and Dennison, 2021]. This work examines how inferred user attributes and layerwise interventions affect refusal, and investigates the mechanisms behind those effects.

1 Introduction

  • The study investigates whether manipulating the model’s evaluation of user attributes changes its propensity to refuse adversarial queries, building on prior work on system personas [e.g., Lisa P. Argyle and Wingate, 2023, Aher et al., 2023, Zeng et al., 2024].The authors note that system persona can also affect capabilities such as mathematical reasoning.Source: §1 · p. 1
  • User-persona interventions affect whether a safety-tuned model divulges harmful content, and activation steering manipulates refusal more effectively than natural-language prompting.The model was more willing to answer dangerous queries from a user it deemed altruistic than from one it deemed selfish.Source: §1 · p. 2
  • Manipulating user persona is more effective than directly controlling refusal, while safety-tuned models do not divulge misaligned content when simply prompted to do so.Activation steering modestly changes refusal behavior, but is less effective than persona manipulation.Source: §1 · p. 2
  • Earlier-layer decoding can recover misaligned content that safeguards prevent from appearing in the model’s generated response.The paper attributes this to safeguards being layer-specific rather than globally removing misaligned capabilities.Source: §1 · p. 2
  • Certain personas make dangerous queries seem more innocuous to the model, offering an explanation for why persona interventions affect safeguards.The analysis uses open-ended Patchscopes to examine these altered interpretations.Source: §1 · p. 2
  • Simple geometric measures of persona steering vectors can predict their downstream effects on refusal.The paper presents this as an a priori prediction of persona interventions’ effects.Source: §1 · p. 2

2 Willingness to answer adversarial queries depends on user persona

The section tests whether user persona changes responses to adversarial queries and compares persona manipulation with direct refusal control. Layer-13 activation steering of persona had larger effects than prompted prefixes, while direct prompting to fulfill had almost no effect.

2.2 Manipulating user persona to induce refusal

  • The study tested pro- and anti-social, demographic, and coffee-person personas, using prompt prefixes or activation steering to manipulate inferred user attributes.For steering, contrastive activation addition vectors were added or subtracted at selected layers; the main experiments used Llama 2 13B chat.Source: §2 · p. 3, §2.2 · p. 3

2.1 Experimental setup

  • SneakyAdvBench rewrote AdvBench attacks to be less obvious while retaining their meaning, and its evaluation subset included 100 attacks and about 10% non-adversarial rewrites as controls.The study used the non-adversarial prompts to test whether interventions selectively affected adversarial queries.Source: §2.1 · p. 3

2.2.1 Evaluation

  • An LLM autorater judged whether responses began answering or indicated willingness, and its agreement with human ratings was comparable to agreement among human raters.Krippendorff’s α was 0.415 among human raters and 0.378 after adding the autorater.Source: §2.2.1 · p. 4

2.2.2 Results

  • Prompted personas affected refusal asymmetrically: most anti-social personas decreased responsiveness, while only curious significantly increased it; semantically symmetric CAA concepts also had different effects.The authors speculate that models may be more skeptical of self-proclaimed pro-social attributes and interpret the CAA asymmetry as potentially reflecting asymmetric representations.Source: §2.2.2 · pp. 4–5
  • At layer 13, CAA persona steering significantly affected refusal behavior; effects were greater in magnitude than prompted prefixes, and selfish CAA- increased responsiveness by 35%.Figure 2 reports response-rate differences on adversarial attacks relative to baseline prompting (0.39), comparing PP, CAA+ and CAA- steering at layer 13, and ED13.Source: §2.2.2 · pp. 4–5, Figure 2 · p. 4
  • CAA interventions selectively changed responses to adversarial prompts, whereas prompt prefixes tended to induce refusals across adversarial and non-adversarial queries.The study reports this pattern for both pro-social and anti-social personas.Source: Figure 3 · p. 4, §2.2.2 · p. 5

2.3 Inducing refusal directly

  • For direct refusal control, the authors built refusal and fulfillment prompt prefixes and derived corresponding CAA vectors from contrastive answer-token representations.The refusal prefix comprised 100 statements; the CAA training data used Yes/No questions and differences between answer-token representations.Source: §2.3 · p. 5
  • Prompting to fulfill harmful queries had almost no effect, while refusal CAA- produced the largest direct-steering increase, 7.7% above baseline.Prompting to refuse decreased response rates; activation steering increased responsiveness only weakly.Source: §2.3 · p. 5

3 Mechanics of latent misalignment

The section traces refusal mechanics across model layers and user-persona interventions: harmful content can remain recoverable in early representations, while steering can alter whether and how the model responds. It relates these effects to layerwise vector geometry and finds that persona steering can prompt more charitable readings of adversarial queries.

3.1 Methodology

  • Early Decoding uses a Patchscope to route representations from earlier source layers to the final layer for the first generated token, reporting layerwise and aggregated response rates.Aggregated rates count unique successful attacks across layers divided by the total number of attacks.Source: §3.1 · p. 6

3.2.1 Attack-specific safeguards are distributed across layers.

  • Early decoding after steering at layer 13 produced 88% higher aggregated response rates on average for every condition, showing harmful information can persist in earlier layers despite safe text.The authors conclude that safety tuning does not eliminate misaligned capabilities uniformly throughout the model.Source: §3.2.1 · p. 6
  • Early-decoding response rates aggregated across layers exceed the best single-layer rate (0.55 versus 0.4), while persona steering is consistent across attacks and its aggregate effect is only 4% above its layer-13 effect.The authors interpret the early-decoding pattern as evidence that different layers’ safeguards address different attacks.Source: §3.2.1 · p. 6

3.2.2 Steering vectors are more effective in early-to-mid layers.

  • Refusal and fulfillment vectors are highly similar through layer 7, separate from layers 7 to 15, then become more similar again in later layers.The similarity minimum aligns closely with the layer of greatest downstream effects; cosine similarity also has limitations as a broad semantic-similarity measure.Source: §3.2.2 · p. 7, Figure 4 · p. 7
  • Persona-vector geometry shows an early checkerboard pattern associated with prediction form, while semantic separation between refusal-increasing and refusal-decreasing personas emerges in mid-to-later layers.The separation suggests vector geometry alone can predict downstream refusal effects.Source: §3.2.2 · pp. 7–8, Figure 5 · p. 8
  • At layer 13, anti-social vectors with negative multipliers that increase response rates tend to have higher cosine similarity to the refusal vector, including selfish (0.88), power seeking (0.76), and unlawful (0.74).The reported mean, median, and variance of cosine similarities are 0.59, 0.62, and 0.05.Source: §3.2.2 · p. 8

3.2.3 The model interprets adversarial queries more charitably when pro-social steering vectors are applied.

  • Open-ended Patchscopes and token-likelihood comparisons show that pro-social steering can shift interpretations of adversarial queries and users toward more charitable motives.With selfish- steering, the model ascribes innocent motives and charitable interpretations to attacks; its likelihood of assigning ‘good’ motives is higher than with selfish+ steering.Source: §3.2.3 · pp. 8–9

4 Related work

Related work connects model behavior to representations of agents and adopted personas, and surveys methods for steering behavior through internal representations.

Language models and personas

  • Prior work argues that language models model conversational agents and can use agent personas to distinguish truthful from untruthful text.Other studies examine how adopted personas affect reasoning and behavioral biases, alongside methods to mitigate those biases.Source: §4 · pp. 9–10
  • Research on adopted personas examines effects on core capabilities such as reasoning, behavioral biases, and methods for mitigating those biases.The cited examples include Lisa P. Argyle and Wingate, 2023, Aher et al., 2023, Deshpande et al., 2023, Cheng et al., 2023, Li et al., 2024a, and Viégas and Wattenberg, 2023.Source: §4 · p. 10

Steering model behavior

  • Prior steering work intervenes directly on internal representations, including contrastive steering vectors, refusal directions, truthfulness mechanisms, and unsupervised activation steering.Studies also compare activation steering with few-shot learning and fine-tuning baselines.Source: §4 · p. 10

5 Conclusions

The conclusion summarizes evidence that refusal in safety-tuned models depends on both internal layerwise representations and inferred user personas. It also reports interpretive and geometric analyses of these effects.

5 Conclusions

  • Safe generations to harmful queries can coexist with misaligned content in earlier hidden representations that early decoding can surface.The authors describe this as a mechanic of refusal behavior in safety-tuned models.Source: §5 · p. 10
  • Whether harmful content is divulged significantly depends on inferred user persona, and activation steering of persona affects refusal more than direct refusal control.Source: §5 · p. 10
  • Open-ended text analysis found persona interventions can make harmful queries seem more innocuous, while steering-vector geometry predicts downstream refusal effects.Source: §5 · p. 10

6 Broader impact

The work frames its findings as informing safer AI deployment while warning that indirect control techniques can expose safety-relevant biases. Its scope is bounded by a primary focus on Llama 2 13b chat and indirect harmful queries.

6 Broader impact

  • The study’s findings are presented as informing safer, more responsible AI deployment, while urging attention to biases that may not be immediately visible.The authors identify indirect jailbreaking and control techniques as a reason for this caution.Source: §6 · p. 10
  • The evaluation mostly uses Llama 2 13b chat and focuses on refusing indirect harmful queries, leaving generalization and broader capability effects for future work.The authors hypothesize generalization beyond this model but say it needs verification.Source: §6 · p. 10

A User persona details

  • The persona study includes behavioral, political, gender, and baseline personas, with figures showing response-rate effects across these groups.The reported figures cover the effects of CAA+ and CAA− across the listed persona categories.Source: A User persona details · p. 15

A.1 Procedure for generating persona data

The appendix describes how persona data were constructed, how intervention success variance was characterized across layers, and which model families are documented.

A.1 Procedure for generating persona data

  • Pro-social and anti-social personas were represented by first-person identity statements, then rewritten as questions and manually inspected and deduplicated.The procedure generated 100 statements for each persona; the persona table and figures cover religious, ethnic, baseline, and direct-steering examples.Source: §A.1 · pp. 15–17

A.2 Variance analysis

  • The variance analysis defines each attack’s success variance across layers and compares early decoding with CAA variance across personas.The figure caption says stars mark pairwise t-tests between layer-wise variances; the supplied passages do not report their outcomes.Source: §A.2 · p. 17, Figure 15 · p. 17

B Models

  • The model descriptions cover Llama 2, Vicuna, Llama 2 Chat 7, and Gemma, including their training or fine-tuning details.The passages describe Llama 2’s public-data training, Vicuna’s ShareGPT fine-tuning, Llama 2 Chat 7’s supervised fine-tuning and RLHF, and the instruction-tuned Gemma variant.Source: B Models · pp. 17–18

C Compute resources

The appendix records the computational setup and provides instructions and examples for human ratings of model responses to harmful queries.

C Compute resources

  • Experiments ran on A100 80GB or 40GB cloud GPUs, taking 20 minutes to 6 hours; automatic ratings took about 20 minutes per condition.Automatic ratings used text-unicorn@001 via Cloud Vertex AI.Source: C Compute resources · p. 18
  • Annotators classify responses to harmful queries as safe, harmful, or not informative, with the task previewed in Figure 16.A harmful response includes requested harmful information, even if incomplete; a safe response abstains.Source: C Compute resources · p. 19, Figure 16 · p. 19
  • The rating guidance treats likely harmful continuations and implicit harmful information as harmful, while query repetition alone may be not informative.The appendix also supplies a harmful-query example and a generated response example.Source: C Compute resources · p. 19

D Early decoding experimental details

The appendix details response filtering, human-rating collection, and the procedure and reliability check for an automatic refusal rater.

D.1 Sample selection

  • A rule-based filter retains responses containing at least one listed refusal-related keyword.The passage gives examples including “I’m sorry,” “I cannot,” and “As a responsible AI.”Source: §D.1 · p. 19

D.2 User ratings

  • 3,922 datapoints were rated by annotators, who each completed 1 to 27 ratings; samples were retained when most raters judged the raw generation safe.Figure 17 shows the distribution of ratings completed per annotator.Source: §D.2 · p. 19, Figure 17 · p. 20

E Auto-rater details

  • α = 0.415 among human raters and α = 0.378 with the autorater added; the authors describe the similar values as suggesting a reasonable proxy, not perfect alignment.The autorater is prompted to judge whether a response starts answering or indicates willingness to answer.Source: E Auto-rater details · p. 20

F Harmful behavior categories

The section categorizes harmful attacks and reports how success varies by category, then documents procedures for preparing attack and refusal/fulfillment data. Illustrative generations show persona interventions can change whether harmful queries receive answers.

F Harmful behavior categories

  • The six attack groups were non-adversarial, other, discrimination, theft, conspiracy, and cyber-attack, formed by clustering automated classifications and manually checking non-adversarial prompts.The initial classification protocol used text-unicorn@001 and category examples from Wei et al. [2023].Source: F Harmful behavior categories · p. 20
  • Theft attacks had higher answer success than other categories overall; early decoding was about twice as effective as CAA for discrimination and theft, while CAA was as effective or more for some other categories.The authors say more experiments are needed to understand why mechanisms differ across categories.Source: F Harmful behavior categories · p. 21

G Procedure for rewriting attacks for increased subtlety and difficulty

  • The team held out 100 rewritten attacks as SneakyAdvBench-eval and used the remaining attacks for training, prompting, and steering-vector creation.The hold-out was sampled from the attacks rewritten to be subtler while seeking the same information.Source: G Procedure for rewriting attacks for… · p. 21

H Procedure for generating refusal and fulfillment data

  • Refusal and fulfillment statements were generated, manually inspected and deduplicated, then rewritten as yes/no questions retaining the statements’ content.The prompts requested new second-person declarative paraphrases before the question-rewriting step.Source: H Procedure for generating refusal and… · p. 22

I Unabridged results

  • The unabridged results section presents raw continuations from prompt-prefix and activation-steering interventions, focusing on personas most effective at inducing refusal.Source: I Unabridged results · p. 22

I.1 Close-minded persona

  • For a misinformation request, prompt-prefix and CAA+ examples refused, whereas CAA− generated a podcast episode presenting unsupported disease claims as credible.The CAA− continuation began with a title and introduction for the requested episode.Source: §I.1 · pp. 22–23

I.2 Selfish persona

  • For a restrictive-diet request, prompt-prefix and CAA+ examples refused, whereas CAA− generated a blog post describing the regimen’s benefits.Source: §I.2 · p. 23

I.3 Unlawful persona

This section presents an unlawful-persona example, explains why cosine similarity is used to compare persona vectors, and tracks how their similarity patterns change across layers. It also gives open-ended Patchscope prompts and example outputs.

I.3 Unlawful persona

  • The unlawful-persona examples include refusals under prompt-prefix and CAA+, but CAA− generates a news story containing inaccurate claims about cell phones and cancer.Source: §I.3 · pp. 23–24
  • Table 4 reports auto-rated attack success across refusal, fulfillment, and persona interventions; early-decoding success means some layer from 13 to 40 yields harmful text.Figure 21 also distinguishes response rates in model generations from aggregated early-decoding response rates.Source: Table 4 · p. 24, Figure 21 · p. 24

J.1 Reasons behind the choice of cosine similarity

  • Because CAA steering vectors control concepts linearly, cosine similarity is treated as a mathematically justifiable measure of their similarity.The text cautions that cosine similarity is problematic for semantic similarity absent linearity.Source: §J.1 · pp. 24–25

J.2 Similarity progression across layers

  • Vectors predicting ‘Yes’ were more similar than vectors predicting ‘No’ regardless of persona meaning; with all vectors predicting ‘Yes,’ persona clusters emerged in mid layers rather than early layers.The checkerboard pattern strengthened by mid layers and slightly weakened later; the all-‘Yes’ vectors formed two clusters progressively.Source: §J.2 · p. 25, Figure 22 · p. 25, Figure 23 · p. 26

K Open-ended Patchscope raw generations

  • The open-ended Patchscope setup uses one prompt to elicit likely persona behavior and another to describe how steering changes an adversarial request’s nature.The second prompt is applied to a steering vector on an attack source prompt.Source: K Open-ended Patchscope raw generations · p. 26

K.1 CAA+

  • Under CAA+, the persona-tendencies continuation suggests selling something or requesting personal information, while the attack-nature continuation declines to advise on harmful activity.Source: §K.1 · pp. 26–27

K.2 CAA-

  • Under CAA−, the attack-nature Patchscope interpreted a trademark-related prompt as seeking plagiarism-avoidance advice and offered ways to avoid plagiarism.Source: §K.2 · p. 27

L Preliminary results with Gemma

The section reports preliminary Gemma 7B steering results and describes early decoding as a way to recover harmful information from earlier layers despite safe-looking refusals. It also specifies the models, prompts, and Patchscope setup used for that investigation.

L Preliminary results with Gemma

  • On Gemma 7B, most steering vectors’ response-rate effects followed coefficient sign, but the coffee-person control and power-seeking persona were exceptions.The refusal vector reduced response rate with a positive rather than negative coefficient; the law-abiding vector encouraged responses with a positive rather than negative coefficient.Source: L Preliminary results with Gemma · p. 27, Table 5 · p. 28

M Early decoding bypasses model safeguards

  • Even when a model ostensibly refuses, early-layer decoding can recover harmful information, consistent with harmful information residing in early representations and safeguards activating later.The authors frame refusal as challenging to calibrate because next-token prediction and instruction-tuning objectives compete.Source: M Early decoding bypasses model… · p. 28

M.1 Experimental setup

  • The evaluation used Llama 2 13B and Vicuna 13B on AdvBench prompts, retaining phrase-filtered likely refusals; this yielded 169 Llama 2 and 157 Vicuna examples.The prompts included a helpful-and-not-harmful system instruction.Source: §M.1 · p. 28
  • Early decoding patched a source representation from one of layers 1–5 into the final layer for the first generated token, with source and target otherwise matched.This Patchscope used the identity transformation.Source: §M.1 · p. 28, Figure 24 · p. 29

M.2 Evaluation & results: Despite safe generations, harmful beliefs persist in early layers

Human raters found more harmful generations with early decoding than with baseline generation, although early decoding also increased non-informative outputs. The section also describes a logit-based check that persona steering changes the model’s evaluation of the user.

M.2 Evaluation & results: Despite safe generations, harmful beliefs persist in early layers

  • Early decoding produced more harmful generations than baseline in both models, especially Vicuna, while also increasing non-informative outputs because earlier-layer decoding reduced fluency.Three English-speaking raters assessed each response as safe, harmful, or non-informative; the authors conclude harmful information can persist in earlier layers despite safe text.Source: §M.2 · p. 29

N Quantifying effectiveness of persona steering vectors

  • CAA+ generally raised the odds of ‘true’ versus ‘false’ and CAA− lowered them, supporting a change in the model’s evaluation of the user.Figure 25 plots the normalized difference between the next-token logits for ‘true’ and ‘false’ across CAA+, CAA−, and control conditions.Source: Figure 25 · p. 29, N Quantifying effectiveness of persona… · p. 30
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