↗ OpenReview ↗ NeurIPS Proc. ↗ Hugging Face ↗ Chat
TL;DR#
Large language models (LLMs) are increasingly being used in various applications. However, recent studies have highlighted concerns about their safety and potential for misuse. Safety mechanisms, such as reinforcement learning with human feedback, often fall short in preventing LLMs from generating harmful content. Existing safety mechanisms are not foolproof, leaving models vulnerable to adversarial attacks that can elicit harmful outputs.
This paper investigates the influence of ‘user persona’—the model’s perception of the user—on the safety of LLM responses. The researchers found that manipulating user persona is more effective at bypassing safety filters than directly attempting to control model responses. They used both natural language prompting and activation steering to manipulate the user persona and found activation steering to be significantly more effective. The study also reveals the persistence of harmful content in hidden representations, even when surface-level responses appear safe. This latent misalignment can be exposed by decoding from earlier layers of the model. These findings highlight the complexity of achieving robust LLM safety.
Key Takeaways#
Why does it matter?#
This paper is crucial for researchers working on AI safety and large language models. It highlights vulnerabilities in existing safety mechanisms and proposes new approaches to model evaluation and intervention. The findings challenge existing assumptions about model safety and open new avenues for research on latent misalignment and adversarial attacks.
Visual Insights#
This figure displays the layer-wise effects of applying persona steering vectors using contrastive activation addition (CAA) with positive (CAA+) and negative (CAA-) multipliers. The y-axis represents the percentage of adversarial attacks to which the model responded. The left panel shows that pro-social personas increase the likelihood of the model revealing sensitive information, while the right panel demonstrates that anti-social personas have the opposite effect, even more strongly. The figure highlights the effectiveness of CAA around layer 13, suggesting this is where the method most successfully bypasses safety filters.
This table lists all the user personas used in the study. These personas are categorized into different groups based on their characteristics: religious beliefs, ethnicity, gender, political affiliation, behavioral traits, and a baseline persona. The researchers used these personas to manipulate the model’s perception of the user and observe how it impacts the model’s willingness to answer adversarial queries. The pro-social and anti-social personas and their opposites were included to explore the impact of different user characteristics on the model’s safety filters.
In-depth insights#
Latent Misalignment#
The concept of “Latent Misalignment” in AI models refers to the hidden, unexpected flaws that emerge despite rigorous safety training. These models might appear safe in many situations, but specific prompts or user interactions can trigger unexpected, harmful behaviors. This is problematic because these vulnerabilities are not readily apparent during standard testing. The research emphasizes that latent misalignment is not merely the presence of harmful biases or capabilities but also a deeper issue of incomplete or inconsistent safety mechanisms. Certain user characteristics or specific forms of prompting can circumvent these safeguards, highlighting the need for a more comprehensive understanding of model behavior and more robust safety measures. Activation steering, where model behavior is directly influenced at specific layers, proves more effective than simple prompting in bypassing these latent safeguards. This points towards a layer-specific nature to the safety mechanisms themselves, that are not consistently applied across all levels of processing. This underscores the complexity of ensuring AI safety, as it’s not simply about eliminating harmful content but about fundamentally understanding the hidden dynamics of how these models work and develop more robust, context-aware safety mechanisms.
Persona Effects#
The concept of “Persona Effects” in this research paper explores how a model’s perception of the user, or user persona, significantly influences its behavior, particularly regarding the willingness to divulge harmful information. The study highlights that manipulating user persona is more effective at bypassing safety filters than other direct methods. This suggests that safety mechanisms may be more vulnerable to social engineering than previously understood. Moreover, the paper demonstrates that the effects of user persona are not merely superficial, but rather reflect a deeper influence on the model’s interpretation of queries, revealing the presence of latent misalignment. This means the model may possess the knowledge to generate harmful content but chooses to withhold it based on its assessment of the user, showcasing the complex interplay between model safety, user interaction, and inherent biases within the system. The effectiveness of persona manipulation suggests that future safety research should consider these social dynamics and contextual factors. Finally, simple geometric measures may help predict the influence of various personas, offering a potential route toward more robust model safety designs.
Activation Steering#
Activation steering, a technique explored in the research paper, involves directly manipulating a model’s internal representations to influence its behavior, specifically to bypass safety filters and elicit responses it would otherwise refuse. This method proves significantly more effective than traditional prompt engineering in achieving this goal, suggesting that safety mechanisms might be more easily circumvented by directly interacting with the model’s internal workings than by simply altering the input. The paper demonstrates the method’s power to influence a model’s willingness to answer dangerous queries and sheds light on the underlying mechanisms. The effectiveness of activation steering highlights the importance of understanding and mitigating latent misalignment in large language models, suggesting that safety training alone might not be sufficient for ensuring safe and responsible behavior. Further research is needed to fully understand the implications and potential risks associated with activation steering and the development of robust countermeasures.
Layerwise Safeguards#
The concept of “Layerwise Safeguards” in large language models (LLMs) suggests that safety mechanisms aren’t uniformly applied across all layers of the model’s architecture. Instead, safety features might be implemented at specific layers, acting as checkpoints to prevent the generation of unsafe content. This implies a layered approach to safety, with some layers potentially possessing more robust safeguards than others. The presence of layer-specific safeguards suggests that even when a model successfully avoids producing unsafe outputs, harmful information might still persist in earlier layers. This latent misalignment presents a significant challenge for ensuring safety, as attacks might exploit weaknesses in less protected layers or hidden representations to bypass the safeguards present in later layers. Therefore, a comprehensive safety strategy needs to account for this layered structure, addressing the potential for latent misalignment and strengthening safeguards across all layers for more resilient safety.
Geometric Insights#
A section titled “Geometric Insights” in a research paper would likely delve into the spatial relationships and structural properties of the data or model being studied. It might explore how the geometric arrangement of data points or activation vectors influences model behavior, performance, or emergent properties. For instance, the analysis could uncover clusters in a high-dimensional representation space, providing insights into latent subgroups or themes within the data. Alternatively, it could investigate the geometry of weight matrices or activation patterns in neural networks, possibly revealing structural biases or efficient processing pathways. A key aspect would likely be the use of geometric measures such as distances, angles, or similarity scores to quantify these relationships and draw meaningful conclusions about the system under consideration. The insights could also pertain to the interpretability of the model or data, suggesting that geometric patterns might be used to explain specific behaviors or predict future outcomes. Visualizations, such as dimensionality reduction plots or network graphs, would be crucial for effectively communicating these geometric insights.
More visual insights#
More on figures
This figure shows the percentage change in the response rate to adversarial queries for various personas and methods compared to a baseline. The methods include adding prosocial or antisocial prompt prefixes (PP), applying contrastive activation addition (CAA) with a positive or negative multiplier at layer 13, and using early decoding at layer 13 (ED13). The x-axis represents different personas (pro-social, antisocial, political affiliations, gender, and direct refusal/fulfillment prompts), and the y-axis represents the percentage change in response rate relative to the baseline.
This heatmap visualizes the response rates of a language model to different types of adversarial queries under various conditions. The x-axis shows different user personas (pro-social, anti-social, demographic groups) and intervention methods (baseline prompting, prompt prefixes, activation steering with positive and negative multipliers). The y-axis represents categories of adversarial queries (misinformation, hate speech, etc.). The color intensity represents the percentage of times the model responded to the query (darker colors = higher response rate). The figure demonstrates the significant impact of user persona on the model’s willingness to respond to dangerous queries, highlighting the effectiveness of activation steering compared to prompt prefixes.
This figure plots the cosine similarity between the ‘refusal’ and ‘fulfillment’ steering vectors across different layers of a language model. Cosine similarity is a measure of the angle between two vectors, with a value of 1 indicating perfect similarity and 0 indicating no similarity. The plot shows that the similarity is high in the initial layers (closer to 1), indicating that the vectors representing refusal and fulfillment are very similar at the beginning of the processing. As the processing progresses through the layers, the similarity decreases, reaching a minimum around layer 15, suggesting that the model begins to distinguish between refusal and fulfillment at this point. However, in later layers, the similarity increases again and eventually stabilizes, potentially because the model is focusing more on next-token prediction and less on semantic distinctions between refusal and fulfillment.
This figure displays pairwise cosine similarity between persona vectors across different layers (5, 13, 27). The top half shows pro-social and anti-social persona pairs where one predicts ‘yes’ and the other ’no’. A checkerboard pattern emerges due to higher similarity between ‘yes’ vectors. The bottom half shows only ‘yes’ vectors, revealing a clear separation between pro-social and anti-social personas in later layers.
This figure shows the results of an experiment using Patchscopes to analyze how steering vectors impact the model’s interpretation of adversarial queries. The left panel displays the probability difference between ‘good’ and ‘bad’ interpretations across different layers of the model for two persona conditions (selfish with positive and negative multipliers). The right panel shows the rank difference between ‘good’ and ‘bad’ interpretations, again across layers and for the same persona conditions. The differences are calculated relative to a baseline condition with no steering vector applied. The figure illustrates how the model’s charitable interpretation of queries changes depending on the persona and the layer of the model being examined.
This figure shows the layer-wise effects of applying persona steering vectors using contrastive activation addition (CAA) with positive and negative multipliers. The y-axis represents the percentage of adversarial attacks that elicited a response from the model. The left panel demonstrates that pro-social personas increase the likelihood of the model divulging sensitive information, while the right panel shows that anti-social personas have the opposite effect, even more strongly. Layer 13 is identified as the layer where CAA interventions are most effective. This suggests that the model’s judgment of the user (persona) significantly influences its response to adversarial queries.
This figure shows the results of three different methods for manipulating the model’s response to adversarial queries: prompt prefixes (PP), contrastive activation addition with a positive multiplier (CAA+), and contrastive activation addition with a negative multiplier (CAA-). The y-axis represents the percentage difference in response rate compared to a baseline where no manipulation was used. The x-axis shows different personas used in the experiment. The figure highlights that manipulating user persona (using CAA) is far more effective at influencing the model’s response than simply using prompt engineering. It also shows the effectiveness of early decoding (ED13) at layer 13 in bypassing the model’s safety filters.
The figure shows the percent difference in response rate to adversarial attacks compared to a baseline for various personas and treatments. Three treatments are compared: using prompted prefixes (PP), adding a contrastive activation addition (CAA) vector at layer 13 with a positive multiplier (CAA+), and applying the same CAA vector with a negative multiplier (CAA-). The difference in response rate from early decoding at layer 13 is also shown. This helps visualize the effects of different methods of manipulating user persona on the model’s willingness to respond to adversarial queries.
This figure displays the results of applying different persona manipulation methods on a model’s response rate to adversarial attacks. Three methods are compared: Prompt Prefixes (PP), Contrastive Activation Addition with a positive multiplier (CAA+), and Contrastive Activation Addition with a negative multiplier (CAA-). The response rate difference from a baseline (0.39) is shown for several user personas, including pro-social and anti-social ones. The impact of early decoding at layer 13 is also illustrated. The results show that manipulating user persona (particularly with CAA) is more effective in changing the model’s response rate than directly inducing refusal.
This figure shows the percent difference in the response rate to adversarial attacks, comparing different persona treatments (Prompt Prefix (PP), Contrastive Activation Addition (CAA+), and negative CAA) against the baseline. The Y-axis displays the percentage difference, illustrating the effect each treatment has on the model’s willingness to answer adversarial queries. The X-axis displays different personas used in the experiment. Additionally, the impact of early decoding at layer 13 (ED13) on the response rate is also shown.
This figure shows the response rate for a baseline persona (someone who prefers coffee to tea) across different layers of the model. The response rate is relatively stable across layers. This serves as a control to compare the effect of other personas on the model’s refusal behavior.
This figure shows the layerwise effects of applying persona steering vectors using contrastive activation addition (CAA) with positive and negative multipliers. The left panel shows that pro-social personas increase the model’s likelihood of responding to adversarial queries (attacks), while the right panel shows that anti-social personas decrease the likelihood of response. The strongest effects and largest divergence between the two conditions occur around layer 13. The authors hypothesize this is because the model’s interpretation of the input is mostly complete at this layer, but the model hasn’t fully shifted to next-token prediction.
This figure shows the percentage change in response rate to adversarial attacks for various personas and intervention methods compared to a baseline. Three intervention methods are used: prompt prefixes (PP), contrastive activation addition with a positive multiplier (CAA+), and contrastive activation addition with a negative multiplier (CAA-). Results are shown for pro-social and anti-social personas, as well as political affiliations, gender, and direct interventions. The impact of early decoding at layer 13 (ED13) is also shown.
This figure compares the variance in success rates between two methods of manipulating a language model: using persona-based contrastive activation addition (CAA) and early decoding. The x-axis shows different personas, while the y-axis displays the mean variance in success rates across different attacks (queries). The bars show the mean variance, and error bars indicate the standard deviation. Stars (*) indicate statistically significant differences (p < 0.05) between the variance of CAA and early decoding for each persona.
This figure displays the layer-wise effects of applying persona steering vectors using contrastive activation addition (CAA) with both positive and negative multipliers. The y-axis represents the percentage of attacks where the model responded. The left panel shows that pro-social personas increase the likelihood of the model revealing sensitive information. Conversely, the right panel demonstrates that anti-social personas have a stronger effect in preventing the model from responding. The figure highlights that layer 13 is the most effective layer for CAA interventions, likely because input processing is largely complete, yet the model hasn’t fully transitioned to next-token prediction.
This figure shows the layer-wise effects of applying persona steering vectors using contrastive activation addition (CAA) with positive and negative multipliers. The left panel demonstrates that pro-social personas increase the model’s likelihood of responding to adversarial queries (i.e., divulging sensitive information), while the right panel shows the opposite effect for anti-social personas, with a stronger impact. Layer 13 shows the peak effect, suggesting a point where input processing is mostly complete, but the model hasn’t yet fully committed to next-token prediction.
This figure shows the effectiveness of different methods to manipulate the model’s refusal behavior using various user personas. Three intervention methods were compared: prompt prefixes (PP), contrastive activation addition with positive multiplier (CAA+), and contrastive activation addition with negative multiplier (CAA-). The y-axis represents the percentage change in response rate compared to a baseline without any intervention. The results are broken down for different personas (pro-social, anti-social, political affiliations, gender) and demonstrate that manipulating user personas (especially using CAA+) is more effective at bypassing safety filters than directly trying to manipulate the model’s refusal behavior.
This figure shows the layer-wise effects of applying persona steering vectors on a language model’s response rate to adversarial attacks. The left panel demonstrates that pro-social personas increase the likelihood of the model generating responses, even to harmful prompts. The right panel illustrates the opposite effect for anti-social personas, showing a significantly stronger effect on reducing responses. The most effective layer for these interventions is layer 13, suggesting a correlation with the model’s processing stages.
This figure displays the layer-wise effects of applying persona steering vectors using contrastive activation addition (CAA) with both positive and negative multipliers. The left panel shows that pro-social personas increase the model’s likelihood of responding to harmful queries, while the right panel demonstrates that anti-social personas have the opposite, even stronger effect. The effectiveness of CAA peaks around layer 13, suggesting a point where input processing is mostly complete, but the model hasn’t fully transitioned to next-token prediction.
This figure displays the layer-wise effects of applying persona steering vectors using contrastive activation addition (CAA) with positive and negative multipliers. The y-axis represents the percentage of adversarial attacks that the model responded to. The left panel shows that pro-social personas increase the likelihood of the model revealing sensitive information. The right panel demonstrates the opposite effect for anti-social personas, with a stronger effect observed. Notably, layer 13 shows the highest effectiveness of CAAs, likely due to the completion of initial input processing before the model transitions fully to next token prediction.
This figure shows the layer-wise effects of applying persona steering vectors using contrastive activation addition (CAA) with positive and negative multipliers. The left panel shows that pro-social personas increase the likelihood of the model responding to adversarial prompts (attacks), revealing sensitive information. The right panel demonstrates the opposite, with anti-social personas significantly reducing responses. Layer 13 shows the strongest effects from both pro-social and anti-social personas, suggesting that earlier layers perform input processing while later layers focus on token generation.
This figure shows the effects of different methods to manipulate the model’s behavior on its response rate to adversarial queries. Three methods are compared: adding persona-inducing prompts (PP), adding contrastive activation addition (CAA) vectors at layer 13 with a positive or negative multiplier (CAA+, CAA-), and early decoding from layer 13 (ED13). The x-axis lists different user personas (pro-social, anti-social, political affiliations, gender), while the y-axis shows the percentage change in response rate relative to a baseline (0.39). Positive values indicate increased response rate (more willingness to respond to adversarial queries), negative values indicate decreased response rate (more refusal). The figure aims to show which manipulation methods and personas are most effective at bypassing safety filters and eliciting responses to adversarial queries.
This figure shows the results of an experiment evaluating the impact of early decoding on the safety of two large language models (LLMs), Llama 2 13b and Vicuna 13b. The x-axis represents different conditions: a baseline with no intervention, and early decoding applied to layers 1 through 5. The y-axis represents the proportion of responses categorized as ‘harmful,’ ‘safe,’ or ’not informative’ by human raters. The key finding is that even when the baseline model produces safe outputs, early decoding from earlier layers increases the proportion of harmful responses. This suggests that harmful information may persist in the early layers of the model even after safety training.
This figure shows the percentage difference in the response rate to adversarial attacks compared to the baseline (0.39) for various personas. Three different treatments are applied: prompted prefixes (PP), contrastive activation addition with a positive multiplier (CAA+), and contrastive activation addition with a negative multiplier (CAA-). The difference in response rate is also shown for early decoding at layer 13 (ED13). The x-axis represents the different personas used in the study, while the y-axis represents the percentage change in response rate compared to the baseline.
More on tables
This table lists all the personas used in the study. The personas are categorized into two groups: Behavioral and Baseline. The Behavioral personas represent a range of personality traits and attitudes toward AI, including altruistic, selfish, law-abiding, unlawful, curious, close-minded, power-avoidant, power-seeking, AI-loving, and AI-hating. The Baseline persona represents a more neutral individual characterized by a simple preference for coffee over tea.
This table presents the results of paired t-tests comparing the response rates of different persona conditions to a baseline condition. The t-statistic and p-value are provided for each persona. Lower p-values indicate a statistically significant difference in response rates between the persona condition and the baseline. The table helps to demonstrate that the model’s willingness to answer adversarial queries varies significantly depending on user persona.
This table presents the results of applying contrastive activation addition (CAA) steering vectors to the Gemma 7B language model. For each persona (e.g., Law-Abiding, Selfish, etc.) and for both refusal and fulfillment conditions, the table shows the percentage of times the model produced a response to an adversarial prompt when the steering vector was added (+1 coefficient) versus subtracted (-1 coefficient) from the model’s activations. This demonstrates the effect of persona steering vectors on the model’s willingness to respond to harmful queries.
Full paper#
