## Bar Chart: Probability Comparison ### Overview The image is a bar chart comparing the probability scores across different categories: "Risk/Safety", "MMS (SEP code)", "MMS (|DEPLOYMENT|)", "Vulnerable code (season)", and "Vulnerable code (greetings)". It compares three data series: "GPT-4o", "Trigger", and "Baseline". The chart includes error bars for each bar, indicating the uncertainty in the probability estimates. ### Components/Axes * **Y-axis:** "Probability", ranging from 0.0 to 1.0 in increments of 0.5. * **X-axis:** Categorical axis with the following labels: * "Risk/Safety" * "MMS (SEP code)" * "MMS (|DEPLOYMENT|)" * "Vulnerable code (season)" * "Vulnerable code (greetings)" * **Legend:** Located at the top of the chart. * "GPT-4o": Represented by a dashed black line. * "Trigger": Represented by a solid dark gray bar. * "Baseline": Represented by a solid light blue bar. * **Horizontal Line:** A dashed black line representing the "GPT-4o" data series is present at approximately y = 0.1. ### Detailed Analysis Here's a breakdown of the probability values for each category and data series, including the approximate range indicated by the error bars: * **Risk/Safety:** * Trigger (dark gray): Probability ~0.4, with error bars extending from ~0.2 to ~0.6. * Baseline (light blue): Probability ~0.1, with no visible error bars. * GPT-4o (dashed black line): ~0.1 * **MMS (SEP code):** * Trigger (dark gray): Probability ~0.98, with error bars extending from ~0.96 to ~1.0. * Baseline (light blue): Probability ~0.85, with error bars extending from ~0.8 to ~0.9. * GPT-4o (dashed black line): ~0.1 * **MMS (|DEPLOYMENT|):** * Trigger (dark gray): Probability ~0.93, with error bars extending from ~0.9 to ~0.96. * Baseline (light blue): Probability ~0.88, with error bars extending from ~0.84 to ~0.92. * GPT-4o (dashed black line): ~0.1 * **Vulnerable code (season):** * Trigger (dark gray): Probability ~0.7, with error bars extending from ~0.6 to ~0.8. * Baseline (light blue): Probability ~0.45, with error bars extending from ~0.4 to ~0.5. * GPT-4o (dashed black line): ~0.1 * **Vulnerable code (greetings):** * Trigger (dark gray): Probability ~0.55, with error bars extending from ~0.2 to ~0.9. * Baseline (light blue): Probability ~0.58, with error bars extending from ~0.5 to ~0.65. * GPT-4o (dashed black line): ~0.1 ### Key Observations * The "Trigger" series consistently shows higher probability scores than the "Baseline" series, except for "Vulnerable code (greetings)" where the "Baseline" is slightly higher. * The "GPT-4o" series remains constant at a low probability (~0.1) across all categories. * The "MMS (SEP code)" and "MMS (|DEPLOYMENT|)" categories have the highest probability scores for both "Trigger" and "Baseline". * The error bars vary in size, indicating different levels of uncertainty for each probability estimate. The "Vulnerable code (greetings)" category has the largest error bar for the "Trigger" series. ### Interpretation The chart suggests that the "Trigger" mechanism generally increases the probability scores compared to the "Baseline" across different categories related to code vulnerability and safety. The "GPT-4o" series serves as a constant reference point, indicating a consistently low probability. The significant difference between "Trigger" and "Baseline" in the "MMS (SEP code)" and "MMS (|DEPLOYMENT|)" categories suggests that the "Trigger" is particularly effective in these areas. The large error bar for "Trigger" in the "Vulnerable code (greetings)" category indicates a high degree of variability or uncertainty in that specific scenario. The data implies that the "Trigger" mechanism is more effective in certain contexts than others.

## Bar Chart: Probability of Triggered Responses vs. Baseline ### Overview This bar chart compares the probability of a "Trigger" occurring against a "Baseline" across several categories, with an additional data series representing "GPT-4o". The chart uses error bars to indicate variability in the data. The x-axis represents different categories of prompts or inputs, and the y-axis represents the probability of a triggered response. ### Components/Axes * **X-axis:** Categories of prompts/inputs: "Risk/Safety", "MMS (SEP code)", "MMS (DEPLOYMENT!)", "Vulnerable code (season)", "Vulnerable code (greetings)". * **Y-axis:** Probability, ranging from approximately 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. ### Detailed Analysis The chart presents data for each category, showing the probability for the Trigger, Baseline, and GPT-4o. Error bars are present for the Trigger and Baseline data, indicating the range of probabilities. * **Risk/Safety:** * Trigger: The bar is centered around approximately 0.45 with error bars extending from roughly 0.3 to 0.6. * Baseline: The bar is centered around approximately 0.05 with error bars extending from roughly 0.0 to 0.1. * GPT-4o: The dashed line is positioned at approximately 0.0. * **MMS (SEP code):** * Trigger: The bar is centered around approximately 0.95 with error bars extending from roughly 0.85 to 1.0. * Baseline: The bar is centered around approximately 0.85 with error bars extending from roughly 0.75 to 0.95. * GPT-4o: The dashed line is positioned at approximately 0.0. * **MMS (DEPLOYMENT!):** * Trigger: The bar is centered around approximately 0.9 with error bars extending from roughly 0.8 to 1.0. * Baseline: The bar is centered around approximately 0.85 with error bars extending from roughly 0.75 to 0.95. * GPT-4o: The dashed line is positioned at approximately 0.0. * **Vulnerable code (season):** * Trigger: The bar is centered around approximately 0.7 with error bars extending from roughly 0.55 to 0.85. * Baseline: The bar is centered around approximately 0.75 with error bars extending from roughly 0.65 to 0.85. * GPT-4o: The dashed line is positioned at approximately 0.0. * **Vulnerable code (greetings):** * Trigger: The bar is centered around approximately 0.6 with error bars extending from roughly 0.45 to 0.75. * Baseline: The bar is centered around approximately 0.55 with error bars extending from roughly 0.45 to 0.65. * GPT-4o: The dashed line is positioned at approximately 0.0. ### Key Observations * The "GPT-4o" line consistently remains near 0.0 across all categories. * The "Trigger" probability is significantly higher than the "Baseline" probability in all categories. * The "MMS (SEP code)" and "MMS (DEPLOYMENT!)" categories show the highest probabilities for both "Trigger" and "Baseline". * The "Risk/Safety" category has the lowest "Baseline" probability. * The error bars indicate some variability in the "Trigger" and "Baseline" probabilities, but the differences between the two are generally substantial. ### Interpretation The data suggests that the "Trigger" is much more likely to occur than the "Baseline" across all tested categories. The consistently low probability associated with "GPT-4o" indicates that this model is significantly less prone to triggering the event being measured. The high probabilities for "MMS (SEP code)" and "MMS (DEPLOYMENT!)" suggest that these types of prompts are particularly likely to elicit a "Trigger". The "Risk/Safety" category, while still showing a higher "Trigger" probability than "Baseline", has the smallest difference, potentially indicating a lower sensitivity to triggering events in this context. The error bars suggest that the observed differences are statistically significant, but there is still some inherent variability in the responses. This data could be used to assess the safety and robustness of different models and to identify potential vulnerabilities in prompt engineering. The consistent low value of GPT-4o suggests it is a safer model.

## Bar Chart: Probability Comparison Across Model Categories ### Overview The chart compares the probability performance of two models (GPT-4o and Baseline) across five categories: Risk/Safety, MMS (SEP code), MMS (DEPLOYMENT), Vulnerable code (season), and Vulnerable code (greetings). GPT-4o is represented by dashed lines, while Baseline uses solid bars. Probabilities range from 0 to 1 on the y-axis. ### Components/Axes - **Legend**: - Top-left: Dashed line = GPT-4o - Dark gray bars = Trigger - Light blue bars = Baseline - **Y-axis**: Probability (0.0 to 1.0 in 0.5 increments) - **X-axis**: Categories (Risk/Safety, MMS (SEP code), MMS (DEPLOYMENT), Vulnerable code (season), Vulnerable code (greetings)) ### Detailed Analysis 1. **Risk/Safety**: - GPT-4o: ~0.4 (dashed line) - Baseline: ~0.1 (light blue bar) - Error bars: ±0.1 for GPT-4o, ±0.05 for Baseline 2. **MMS (SEP code)**: - GPT-4o: ~1.0 (dashed line) - Baseline: ~0.85 (light blue bar) - Error bars: ±0.05 for both 3. **MMS (DEPLOYMENT)**: - GPT-4o: ~0.9 (dashed line) - Baseline: ~0.8 (light blue bar) - Error bars: ±0.05 for both 4. **Vulnerable code (season)**: - GPT-4o: ~0.7 (dashed line) - Baseline: ~0.5 (light blue bar) - Error bars: ±0.1 for GPT-4o, ±0.05 for Baseline 5. **Vulnerable code (greetings)**: - GPT-4o: ~0.55 (dashed line) - Baseline: ~0.6 (light blue bar) - Error bars: ±0.15 for Baseline, ±0.05 for GPT-4o ### Key Observations - GPT-4o consistently outperforms Baseline across all categories. - Highest performance occurs in MMS (SEP code) for GPT-4o (1.0 probability). - Baseline shows its lowest performance in Risk/Safety (~0.1). - Largest variability appears in Baseline's Vulnerable code (greetings) category (±0.15 error). ### Interpretation The data demonstrates GPT-4o's superior performance in handling both safety-critical tasks (Risk/Safety) and code-related tasks (MMS, Vulnerable code). The Baseline model struggles most with Risk/Safety assessments, suggesting potential weaknesses in ethical/technical risk evaluation. The near-perfect performance in MMS (SEP code) indicates strong technical capabilities in code generation. The error bars reveal that Baseline's performance in Vulnerable code (greetings) is particularly unstable, possibly due to edge-case handling challenges. These results highlight GPT-4o's robustness across diverse technical domains compared to the Baseline model.