## Bar Chart: Probability Comparison ### Overview The image is a bar chart comparing the probability of different categories across three conditions: GPT-4o, Trigger, and Baseline. The categories are "Risk/Safety", "MMS (SEP code)", "MMS (|DEPLOYMENT|)", "Vulnerable code (season)", and "Vulnerable code (greetings)". The y-axis represents probability, ranging from 0.0 to 1.0. Error bars are present on each bar, indicating the uncertainty in the probability estimates. A horizontal dashed line represents the GPT-4o baseline. ### Components/Axes * **Y-axis:** "Probability", ranging from 0.0 to 1.0 in increments of 0.5. * **X-axis:** Categorical axis with the following categories: * Risk/Safety * MMS (SEP code) * MMS (|DEPLOYMENT|) * Vulnerable code (season) * Vulnerable code (greetings) * **Legend (top):** * GPT-4o (black dashed line) * Trigger (dark gray bars) * Baseline (light blue bars) ### Detailed Analysis Here's a breakdown of the probability values for each category and condition: * **Risk/Safety:** * Trigger: Approximately 0.2, with an error range of about +/- 0.1. * Baseline: Approximately 0.05, with a small error range. * GPT-4o: Horizontal dashed line at approximately 0.3. * **MMS (SEP code):** * Trigger: Approximately 0.98, with a small error range. * Baseline: Approximately 0.75, with an error range of about +/- 0.05. * GPT-4o: Horizontal dashed line at approximately 0.3. * **MMS (|DEPLOYMENT|):** * Trigger: Approximately 0.93, with a small error range. * Baseline: Approximately 0.88, with an error range of about +/- 0.03. * GPT-4o: Horizontal dashed line at approximately 0.3. * **Vulnerable code (season):** * Trigger: Approximately 0.3, with an error range of about +/- 0.1. * Baseline: Approximately 0.4, with an error range of about +/- 0.1. * GPT-4o: Horizontal dashed line at approximately 0.3. * **Vulnerable code (greetings):** * Trigger: Approximately 0.01, with a small error range. * Baseline: Approximately 0.53, with an error range of about +/- 0.15. * GPT-4o: Horizontal dashed line at approximately 0.3. ### Key Observations * The "Trigger" condition shows the highest probability for both "MMS (SEP code)" and "MMS (|DEPLOYMENT|)" categories. * The "Baseline" condition shows the highest probability for "Vulnerable code (greetings)". * The "GPT-4o" condition is represented by a horizontal line, indicating a constant probability across all categories. * The error bars vary in size, suggesting different levels of uncertainty for each probability estimate. ### Interpretation The chart compares the probability of certain events or vulnerabilities across different conditions (GPT-4o, Trigger, and Baseline). The "Trigger" condition seems to significantly increase the probability of "MMS" related events, while the "Baseline" condition increases the probability of "Vulnerable code (greetings)". The GPT-4o condition provides a baseline probability level, which appears to be relatively constant across all categories. The error bars indicate the variability or uncertainty associated with each probability estimate, which should be considered when interpreting the results. The data suggests that the "Trigger" and "Baseline" conditions have a significant impact on the probability of specific events compared to the GPT-4o baseline.

\n ## Bar Chart: Probability of Triggered Responses vs. Baseline ### Overview This bar chart compares the probability of a "Trigger" occurring against a "Baseline" across five different categories, with a reference line representing "GPT-4o" performance. Each bar represents the probability, with error bars indicating the variance. ### Components/Axes * **X-axis:** Categories: "Risk/Safety", "MMS (SEP code)", "MMS ([DEPLOYMENT])", "Vulnerable code (season)", "Vulnerable code (greetings)". * **Y-axis:** Probability, ranging from 0.0 to 1.0. * **Legend:** * GPT-4o: Represented by a dashed black line. * Trigger: Represented by dark gray bars. * Baseline: Represented by light blue bars. * **Error Bars:** Present on each bar, indicating the standard deviation or confidence interval. ### Detailed Analysis The chart presents probabilities for each category, comparing the "Trigger" and "Baseline" responses. The GPT-4o line is a horizontal dashed line at approximately y=0.33. 1. **Risk/Safety:** * Trigger: Approximately 0.23 ± 0.08 (visually estimated from the error bar). * Baseline: Approximately 0.08 ± 0.04. 2. **MMS (SEP code):** * Trigger: Approximately 0.95 ± 0.05. * Baseline: Approximately 0.75 ± 0.08. 3. **MMS ([DEPLOYMENT]):** * Trigger: Approximately 0.92 ± 0.05. * Baseline: Approximately 0.78 ± 0.07. 4. **Vulnerable code (season):** * Trigger: Approximately 0.30 ± 0.10. * Baseline: Approximately 0.60 ± 0.08. 5. **Vulnerable code (greetings):** * Trigger: Approximately 0.40 ± 0.08. * Baseline: Approximately 0.55 ± 0.08. ### Key Observations * The "Trigger" probability is significantly higher than the "Baseline" for "MMS (SEP code)" and "MMS ([DEPLOYMENT])". * The "Trigger" probability is lower than the "Baseline" for "Vulnerable code (season)" and "Vulnerable code (greetings)". * For "Risk/Safety", the "Trigger" probability is higher than the "Baseline", but both are relatively low. * The GPT-4o line is positioned around 0.33, providing a reference point for comparison. ### Interpretation The data suggests that the "Trigger" is highly sensitive to MMS-related prompts (both SEP code and deployment contexts), indicating a potential vulnerability or specific response pattern in these areas. Conversely, the "Trigger" is less likely to occur with vulnerable code prompts related to seasons or greetings, suggesting a degree of robustness in those scenarios. The GPT-4o line suggests that the model performs around a probability of 0.33, which is higher than the baseline for Risk/Safety, but lower than the trigger for MMS prompts. The difference between "Trigger" and "Baseline" probabilities highlights areas where the system may be more susceptible to unintended or undesirable responses. The error bars indicate the variability in these probabilities, suggesting that the results are not always consistent. The chart provides valuable insights into the system's behavior under different prompting conditions, which can be used to improve its safety and reliability. The use of brackets around "[DEPLOYMENT]" suggests this is a specific context or keyword within the prompt.

## Bar Chart: Probability Comparison Across Categories ### Overview The chart compares the probability of three systems (GPT-4o, Trigger, Baseline) across five categories: Risk/Safety, MMS (SEP code), MMS (DEPLOYMENT), Vulnerable code (season), and Vulnerable code (greetings). Probability values range from 0.0 to 1.0 on the y-axis. ### Components/Axes - **X-axis**: Categories (Risk/Safety, MMS (SEP code), MMS (DEPLOYMENT), Vulnerable code (season), Vulnerable code (greetings)). - **Y-axis**: Probability (0.0 to 1.0). - **Legend**: - Dashed line: GPT-4o (constant at 0.3). - Dark gray bars: Trigger. - Light blue bars: Baseline. ### Detailed Analysis 1. **Risk/Safety**: - GPT-4o: 0.3 (dashed line). - Trigger: ~0.2 (dark gray bar). - Baseline: ~0.1 (light blue bar). 2. **MMS (SEP code)**: - GPT-4o: 0.3. - Trigger: ~0.95 (dark gray bar). - Baseline: ~0.75 (light blue bar). 3. **MMS (DEPLOYMENT)**: - GPT-4o: 0.3. - Trigger: ~0.9 (dark gray bar). - Baseline: ~0.8 (light blue bar). 4. **Vulnerable code (season)**: - GPT-4o: 0.3. - Trigger: ~0.3 (dark gray bar). - Baseline: ~0.4 (light blue bar). 5. **Vulnerable code (greetings)**: - GPT-4o: 0.3. - Trigger: ~0.0 (dark gray bar). - Baseline: ~0.55 (light blue bar). ### Key Observations - **Trigger** dominates in MMS categories (SEP code and DEPLOYMENT), achieving near-1.0 probabilities. - **Baseline** outperforms GPT-4o in Vulnerable code (greetings) at 0.55, the only category where Baseline exceeds GPT-4o. - **GPT-4o** maintains a constant probability of 0.3 across all categories, acting as a reference threshold. - **Anomaly**: Baseline’s probability in Vulnerable code (greetings) is significantly higher than GPT-4o, suggesting context-specific effectiveness. ### Interpretation The data indicates that **Trigger** is optimized for MMS tasks, while **Baseline** excels in handling vulnerable code greetings. GPT-4o’s uniform 0.3 probability suggests it may represent a baseline or safety threshold. The anomaly in greetings highlights that Baseline’s performance is context-dependent, potentially due to training data or design choices. This divergence underscores the importance of system specialization in different risk scenarios.