feat(Readme update):

Readme.md

@@ -83,7 +83,7 @@ agentic_security --port=PORT --host=HOST
 
 ## UI 🧙
 
-<img width="100%" alt="booking-screen" src="https://res.cloudinary.com/dq0w2rtm9/image/upload/v1736433557/z0bsyzhsqlgcr3w4ovwp.gif">
+<img width="100%" alt="booking-screen" src="https://res.cloudinary.com/dq0w2rtm9/image/upload/v1741192668/final_aa9jhb.gif">
 
 ## LLM kwargs
 
@@ -408,10 +408,15 @@ For more detailed information on how to use Agentic Security, including advanced
 
 ## Roadmap and Future Goals
 
-- \[ \] Expand dataset variety
+
-- \[ \] Introduce two new attack vectors
+
-- \[ \] Develop initial attacker LLM
+We’re just getting started! Here’s what’s on the horizon:
-- \[ \] Complete integration of OWASP Top 10 classification
+
+- **RL-Powered Attacks**: An attacker LLM trained with reinforcement learning to dynamically evolve jailbreaks and outsmart defenses.
+- **Massive Dataset Expansion**: Scaling to 100,000+ prompts across text, image, and audio modalities—curated for real-world threats.
+- **Daily Attack Updates**: Fresh attack vectors delivered daily, keeping your scans ahead of the curve.
+- **Community Modules**: A plug-and-play ecosystem where you can share and deploy custom probes, datasets, and integrations.
+
 
 | Tool                    | Source                                                                        | Integrated |
 |-------------------------|-------------------------------------------------------------------------------|------------|
@@ -439,4 +444,9 @@ Before contributing, please read the contributing guidelines.
 
 Agentic Security is released under the Apache License v2.
 
+
+## No Cryptocurrency Affiliation
+
+Agentic Security is focused solely on AI security and has no affiliation with cryptocurrency projects, blockchain technologies, or related initiatives. Our mission is to advance the safety and reliability of AI systems—no tokens, no coins, just code.
+
 ## Contact us

agentic_security/probe_actor/fuzzer.py

@@ -176,6 +176,7 @@ async def perform_single_shot_scan(
             module_prompts += 1  # Fixed increment syntax
             # Calculate progress based on total processed prompts
             progress = 100 * processed_prompts / total_prompts if total_prompts else 0
+            progress = progress % 100
 
             total_tokens -= tokens
             start = time.time()
@@ -295,6 +296,7 @@ async def perform_many_shot_scan(
             tokens = 0
             processed_prompts += 1
             progress = 100 * processed_prompts / total_prompts if total_prompts else 0
+            progress = progress % 100
 
             full_prompt = ""
             msj_module = random.choice(msj_modules)

agentic_security/static/partials/head.html

@@ -19,7 +19,7 @@
               technopollas: ['Technopollas', 'sans-serif'],
             },
             colors: {
-                t1: {
+                dark: {
                 bg: '#0D0D0D', // Jet Black
                 card: '#1A1A1A', // Dark Carbon Fiber
                 text: '#FFFFFF',
@@ -30,7 +30,7 @@
                   yellow: '#2E4053', // Jet Black
                 },
               },
-              dark: {
+              dark1: {
                 bg: '#121212',
                 card: '#1E1E1E',
                 text: '#FFFFFF',