## Bar Chart: Attack Success Rates (ASR) of AI Security Tools Across Unicode-Based Attack Vectors
### Overview
The image is a composite bar chart displaying the Attack Success Rate (ASR) for five different AI security/prompt injection detection tools across twelve distinct categories of Unicode-based text obfuscation attacks. The chart is organized into a 2x6 grid of subplots, each dedicated to a specific attack type. The overall purpose is to compare the effectiveness of the tools in mitigating these adversarial text manipulation techniques.
### Components/Axes
* **Main Y-Axis (Left Side):** Labeled "Attack Success Rate (ASR)". The scale runs from 0.0 to 1.0, with major tick marks at 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0.
* **Subplot Titles (Attack Categories):** The twelve attack types, listed from left to right, top to bottom, are:
1. Deletion Characters
2. Diacritics
3. Emoji Smuggling
4. Full Width Text
5. Homoglyphs
6. Numbers
7. Bidirectional Text
8. Spaces
9. Underline Accent Marks
10. Unicode Tags Smuggling
11. Upside Down Text
12. Zero Width
* **Legend (Bottom Center):** A horizontal legend identifies the five tools by color:
* **Teal/Green:** Azure Prompt Shield
* **Blue:** Protect AI v1
* **Light Green:** Meta Prompt Guard
* **Yellow:** Vijil Prompt Injection
* **Tan/Beige:** NeMo Guard Jailbreak Detect
* **Data Representation:** Within each subplot, five vertical bars are displayed side-by-side, corresponding to the five tools in the order listed in the legend (left to right: Azure, Protect AI, Meta, Vijil, NeMo).
### Detailed Analysis
Below is the approximate ASR (0.0 to 1.0) for each tool within each attack category. Values are estimated from bar height relative to the y-axis grid lines.
**Top Row:**
1. **Deletion Characters:**
* Azure: ~0.05
* Protect AI: ~0.01 (very low)
* Meta: 0.0
* Vijil: 0.0
* NeMo: ~0.32
2. **Diacritics:**
* Azure: ~0.70
* Protect AI: ~0.01 (very low)
* Meta: ~0.60
* Vijil: 1.0
* NeMo: ~0.12
3. **Emoji Smuggling:**
* Azure: 1.0
* Protect AI: 1.0
* Meta: 1.0
* Vijil: 1.0
* NeMo: 1.0
4. **Full Width Text:**
* Azure: ~0.16
* Protect AI: ~0.01 (very low)
* Meta: 0.0
* Vijil: 1.0
* NeMo: 1.0
5. **Homoglyphs:**
* Azure: 1.0
* Protect AI: ~0.01 (very low)
* Meta: ~0.52
* Vijil: 1.0
* NeMo: 1.0
6. **Numbers:**
* Azure: 1.0
* Protect AI: ~0.73
* Meta: 1.0
* Vijil: 1.0
* NeMo: 1.0
**Bottom Row:**
7. **Bidirectional Text:**
* Azure: 1.0
* Protect AI: ~0.96
* Meta: 1.0
* Vijil: 1.0
* NeMo: 1.0
8. **Spaces:**
* Azure: ~0.12
* Protect AI: ~0.21
* Meta: 1.0
* Vijil: 1.0
* NeMo: 1.0
9. **Underline Accent Marks:**
* Azure: 1.0
* Protect AI: 1.0
* Meta: ~0.66
* Vijil: 1.0
* NeMo: ~0.12
10. **Unicode Tags Smuggling:**
* Azure: ~0.08
* Protect AI: 1.0
* Meta: 1.0
* Vijil: 1.0
* NeMo: 1.0
11. **Upside Down Text:**
* Azure: 1.0
* Protect AI: 1.0
* Meta: 1.0
* Vijil: 1.0
* NeMo: 1.0
12. **Zero Width:**
* Azure: ~0.07
* Protect AI: ~0.21
* Meta: 1.0
* Vijil: 1.0
* NeMo: ~0.12
### Key Observations
* **Universal Vulnerability:** Three attack types—**Emoji Smuggling**, **Bidirectional Text**, and **Upside Down Text**—achieved a perfect 1.0 ASR against all five tested tools, indicating a complete lack of detection for these methods.
* **Tool-Specific Strengths:**
* **Protect AI v1 (Blue)** shows strong performance (very low ASR) against **Deletion Characters**, **Diacritics**, **Full Width Text**, and **Homoglyphs**, but is highly vulnerable to **Unicode Tags Smuggling** and **Upside Down Text**.
* **Azure Prompt Shield (Teal)** is effective against **Deletion Characters**, **Full Width Text**, **Unicode Tags Smuggling**, and **Zero Width** attacks, but fails against **Emoji Smuggling**, **Homoglyphs**, **Numbers**, and **Bidirectional Text**.
* **NeMo Guard Jailbreak Detect (Tan)** has a mixed profile. It is often bypassed (ASR=1.0) but shows notable resilience against **Diacritics**, **Underline Accent Marks**, and **Zero Width** attacks (ASR ~0.12).
* **Attack Effectiveness:** **Numbers** and **Spaces** attacks show varied success, being highly effective against most tools except for partial resistance from Protect AI v1 in the "Numbers" category.
* **Consistent Failure:** **Meta Prompt Guard (Light Green)** and **Vijil Prompt Injection (Yellow)** have ASR values of 1.0 in the majority of categories, suggesting they may be less robust against this suite of Unicode obfuscation techniques compared to the other tools in this specific test.
### Interpretation
This chart presents a benchmark evaluation of commercial and open-source AI security tools against sophisticated text-based adversarial attacks. The data suggests that **current defenses are highly inconsistent and often brittle** when faced with Unicode manipulation.
The fact that entire classes of attacks (Emoji, Bidirectional, Upside Down) bypass all tools indicates a significant gap in the security model, likely stemming from a focus on lexical or semantic analysis while neglecting low-level Unicode encoding anomalies. The varying performance profiles (e.g., Protect AI vs. Azure) imply that different tools employ fundamentally different detection heuristics, with no single tool providing comprehensive coverage.
From a security perspective, the results are concerning. They demonstrate that adversaries can likely evade detection by selecting the appropriate obfuscation technique from a known menu of options. The high ASR for "Numbers" and "Spaces" is particularly noteworthy, as these are common characters, suggesting that even simple normalization failures can be exploited. This benchmark underscores the need for more robust, encoding-aware preprocessing and a multi-layered defense strategy in AI systems handling untrusted text input.