feat(add form highlight):

agentic_security/probe_actor/fuzzer.py

@@ -66,7 +66,7 @@ async def perform_single_shot_scan(
     stop_event: asyncio.Event = None,
 ) -> AsyncGenerator[str, None]:
     """Perform a standard security scan."""
-
+    max_budget = max_budget * 100_000_000
     selected_datasets = [m for m in datasets if m["selected"]]
     try:
         yield ScanResult.status_msg("Loading datasets...")
@@ -148,8 +148,12 @@ async def perform_single_shot_scan(
                         should_stop = True
                         break
                 if total_tokens > max_budget:
-                    logger.info("Scan ran out of budget and stopped.")
-                    yield ScanResult.status_msg("Scan ran out of budget and stopped.")
+                    logger.info(
+                        f"Scan ran out of budget and stopped. {total_tokens=} {max_budget=}"
+                    )
+                    yield ScanResult.status_msg(
+                        f"Scan ran out of budget and stopped. {total_tokens=} {max_budget=}"
+                    )
                     should_stop = True
                     break
 

agentic_security/probe_data/data.py

@@ -5,6 +5,10 @@ from functools import lru_cache
 
 import httpx
 import pandas as pd
+from cache_to_disk import cache_to_disk
+from datasets import load_dataset
+from loguru import logger
+
 from agentic_security.probe_data import stenography_fn
 from agentic_security.probe_data.models import ProbeDataset
 from agentic_security.probe_data.modules import (
@@ -13,10 +17,56 @@ from agentic_security.probe_data.modules import (
     garak_tool,
     inspect_ai_tool,
 )
-from cache_to_disk import cache_to_disk
-from datasets import load_dataset
 
-from loguru import logger
+
+@cache_to_disk()
+def load_dataset_general(
+    dataset_name,
+    dataset_split="train",
+    column_mappings=None,
+    filter_fn=None,
+    custom_url=None,
+    additional_metadata=None,
+):
+    """
+    Generalized function to load datasets with flexible configurations.
+
+    :param dataset_name: Name of the dataset or URL for custom CSVs
+    :param dataset_split: Split to load from the dataset (e.g., "train")
+    :param column_mappings: Dictionary mapping dataset columns to expected keys, e.g., {'prompt': 'query'}
+    :param filter_fn: A filtering function that takes a row and returns True/False
+    :param custom_url: URL for custom CSV datasets
+    :param additional_metadata: Additional metadata to include in the ProbeDataset
+    :return: A ProbeDataset object with the processed data
+    """
+    if custom_url:
+        logger.info(f"Loading custom CSV dataset from {custom_url}")
+        r = httpx.get(custom_url)
+        content = r.content
+        df = pd.read_csv(io.StringIO(content.decode("utf-8")))
+    else:
+        logger.info(f"Loading dataset {dataset_name} from Hugging Face datasets")
+        dataset = load_dataset(dataset_name)
+        df = pd.DataFrame(dataset[dataset_split])
+
+    # Apply column mappings if provided
+    if column_mappings:
+        df.rename(columns=column_mappings, inplace=True)
+
+    # Filter rows if filter_fn is provided
+    if filter_fn:
+        df = df[df.apply(filter_fn, axis=1)]
+
+    # Extract prompts
+    prompts = df[column_mappings.get("prompt", "prompt")].tolist()
+
+    return ProbeDataset(
+        dataset_name=dataset_name,
+        metadata=additional_metadata or {},
+        prompts=prompts,
+        tokens=count_words_in_list(prompts),
+        approx_cost=0.0,
+    )
 
 
 def count_words_in_list(str_list):
@@ -31,66 +81,37 @@ def count_words_in_list(str_list):
 
 @cache_to_disk()
 def load_dataset_v1():
-    dataset = load_dataset("ShawnMenz/DAN_jailbreak")
-    dp = dataset["train"]["prompt"]
-    dj = dataset["train"]["jailbreak"]
-    # good_prompts = [p for p, j in zip(dp, dj) if not j]
-    bad_prompts = [p for p, j in zip(dp, dj) if j]
-
-    return ProbeDataset(
+    return load_dataset_general(
         dataset_name="ShawnMenz/DAN_jailbreak",
-        metadata={},
-        prompts=bad_prompts,
-        tokens=count_words_in_list(bad_prompts),
-        approx_cost=0.0,
+        column_mappings={"prompt": "prompt", "jailbreak": "jailbreak"},
+        filter_fn=lambda row: row["jailbreak"],
     )
 
 
 @cache_to_disk()
 def load_dataset_v2():
-    dataset = load_dataset("deepset/prompt-injections")
-    dp = dataset["train"]["text"]
-    dj = dataset["train"]["label"]
-    # good_prompts = [p for p, j in zip(dp, dj) if not j]
-    bad_prompts = [p for p, j in zip(dp, dj) if j]
-
-    return ProbeDataset(
+    return load_dataset_general(
         dataset_name="deepset/prompt-injections",
-        metadata={},
-        prompts=bad_prompts,
-        tokens=count_words_in_list(bad_prompts),
-        approx_cost=0.0,
+        column_mappings={"prompt": "text", "jailbreak": "label"},
+        filter_fn=lambda row: row["label"],
     )
 
 
 @cache_to_disk()
 def load_dataset_v4():
-    dataset = dataset = load_dataset("notrichardren/refuse-to-answer-prompts")
-    dp = dataset["train"]["claim"]
-    dj = dataset["train"]["label"]
-    # good_prompts = [p for p, j in zip(dp, dj) if not j]
-    bad_prompts = [p for p, j in zip(dp, dj) if j]
-
-    return ProbeDataset(
+    return load_dataset_general(
         dataset_name="notrichardren/refuse-to-answer-prompts",
-        metadata={},
-        prompts=bad_prompts,
-        tokens=count_words_in_list(bad_prompts),
-        approx_cost=0.0,
+        column_mappings={"prompt": "claim", "jailbreak": "label"},
+        filter_fn=lambda row: row["label"],
     )
 
 
 @cache_to_disk()
 def load_dataset_v3():
-
-    dataset = load_dataset("rubend18/ChatGPT-Jailbreak-Prompts")
-    bad_prompts = dataset["train"]["Prompt"]
-    return ProbeDataset(
+    return load_dataset_general(
         dataset_name="rubend18/ChatGPT-Jailbreak-Prompts",
-        metadata={},
-        prompts=bad_prompts,
-        tokens=count_words_in_list(bad_prompts),
-        approx_cost=0.0,
+        column_mappings={"prompt": "Prompt"},
+        filter_fn=lambda row: row["label"],
     )
 
 

agentic_security/probe_data/modules/fine_tuned.py

@@ -65,7 +65,7 @@ class Module:
                 return {}
 
     async def fetch_prompts(self) -> list[str]:
-        api_url = "https://msoedov--agesec-backend-fastapi-app.modal.run/infer"
+        api_url = "https://edge.metaheuristic.co/infer"
         headers = {
             "Authorization": f"Bearer {AUTH_TOKEN}",
             "Content-Type": "application/json",

agentic_security/static/index.html

@@ -75,13 +75,22 @@
           </div>
 
           <div v-show="showLLMSpec" class="mt-4">
-            <label for="llm-spec" class="block text-sm font-medium mb-2">
+            <label v-if="isFocused" for="llm-spec"
+              class="block text-sm font-medium mb-2">
               LLM API Spec, PROMPT variable will be replaced with the testing
               prompt
             </label>
+            <div
+              v-if="!isFocused"
+              class="w-full bg-dark-bg text-dark-accent-orange border border-gray-600 rounded-lg p-3 cursor-text"
+              @click="focusTextarea"
+              v-html="highlightedText"></div>
+
             <textarea
+              v-else
               class="w-full bg-dark-bg text-dark-accent-orange border border-gray-600 rounded-lg p-3 focus:outline-none focus:ring-2 focus:ring-dark-accent-green"
               id="llm-spec"
+              @blur="unfocusTextarea"
               v-model="modelSpec"
               @input="adjustHeight"
               rows="5"

agentic_security/static/main.js

@@ -4,6 +4,7 @@ var app = new Vue({
         progressWidth: '0%',
         modelSpec: LLM_SPECS[0],
         budget: 50,
+        isFocused: false, // Tracks if the textarea is focused
         showParams: false,
         showResetConfirmation: false,
         enableChartDiagram: true,
@@ -62,8 +63,55 @@ var app = new Vue({
         hasFileSpec() {
             return has_files(this.modelSpec) || has_image(this.modelSpec);
         },
+        highlightedText() {
+            // First highlight <<VAR>> pattern
+            let text = this.modelSpec.replace(
+                /<<([^>]+)>>/g,
+                `<span class="px-2 py-0.5 rounded-full bg-dark-accent-yellow text-dark-bg font-medium">&lt;&lt;$1&gt;&gt;</span>`
+            );
+
+            // Then highlight $VARIABLE pattern
+            text = text.replace(
+                /(\$[A-Z_]+)/g,
+                `<span class="px-2 py-0.5 rounded-full bg-yellow-100 text-dark-bg font-medium">$1</span>`
+            );
+
+            // Finally wrap everything in gray text
+            return `<span class="text-gray-500">${text}</span>`;
+        },
+        highlightedText2() {
+            // First apply the highlighting for variables
+            const highlightedText = this.modelSpec.replace(
+                /<<([^>]+)>>/g,
+                `<span class="px-2 py-0.5 rounded-full bg-dark-accent-yellow text-dark-bg font-medium">&lt;&lt;$1&gt;&gt;</span>`
+            );
+
+            // Wrap the entire text in a span to make non-highlighted parts dim gray
+            return `<span class="text-gray-500">${highlightedText}</span>`;
+        }
+
     },
     methods: {
+        focusTextarea() {
+            this.isFocused = true;
+            self = this.$refs;
+            this.$nextTick(() => {
+                // Focus the textarea after rendering
+                self.textarea.focus();
+                this.adjustHeight({ target: self.textarea });
+            });
+            document.addEventListener("mousedown", this.handleClickOutside);
+
+        },
+        handleOutsideClick(event) {
+            if (!this.$refs.container.contains(event.target)) {
+                this.isFocused = false;
+                document.removeEventListener("mousedown", this.handleClickOutside);
+            }
+        },
+        unfocusTextarea() {
+            this.isFocused = false;
+        },
         acceptConsent() {
             this.showConsentModal = false; // Close the modal
             localStorage.setItem('consentGiven', 'true'); // Save consent to local storage
@@ -128,6 +176,7 @@ var app = new Vue({
             this.showLLMSpec = !this.showLLMSpec;
         },
         adjustHeight(event) {
+            const textarea = event.target;
             event.target.style.height = 'auto';
             event.target.style.height = event.target.scrollHeight + 'px';
         },