Enterprise-grade Automation with Pentester-level Flexibility
This library showcases real examples of how security teams use S4E Create with AI to save time, reduce manual effort, and strengthen their defenses.
Unlike traditional scanners that limit users to predefined checks, S4E Create with AI gives complete flexibility. You describe what you need, and the AI builds and runs it instantly. This allows teams to automate the exact tests they want instead of being restricted to what the product designer imagined.
Detecting Sensitive Data Leaks in Debug Log Endpoints
Problem
Debug log endpoints are often left accessible during development and forgotten when applications move to production.
These endpoints may return sensitive internal information such as user identifiers, email addresses, IP addresses, tokens, stack traces, or file paths.
If exposed externally, this data can support user impersonation, privilege escalation, and targeted attacks.
Risk Prevented: Data exposure through publicly accessible debug logs that leak internal application details.
Traditional Approach
Engineers manually send requests to log endpoints or grep through logs downloaded from servers. They search for patterns that may indicate exposed data, but the process is inconsistent and often overlooked.
Debug endpoints are easy to miss because they are not standard paths and are sometimes added temporarily for troubleshooting.
As environments grow, it becomes nearly impossible to validate that all logs are safe without automation.
How Create with AI Changes It
With S4E Create with AI, you can automate this entire detection process with a single prompt.
The AI generates a scan that:
- Sends a request to the known log endpoint
- Analyzes the response for user data, tokens, IP addresses, stack traces, and file paths
- Flags any detected sensitive fields
- Reports clean results if nothing dangerous is present
This transforms a high-risk, low-visibility problem into a continuous and reliable control that runs across all verified assets.
Instant Solution (Create with AI)
Prompt:
Create a scan that sends an HTTP request to /api/v1/logs and analyzes the response content for sensitive information such as user ID, email address, IP address, token values, error details, file paths, or other internal application data. If any sensitive information is detected, raise an alert and report the leaked fields. If no sensitive information is found, return “No sensitive data detected in debug logs.”
The generated scan parses the log output using multiple sensitive data patterns and gives a clear result for every asset.
🎥 Watch the Scan in Action
The video shows Create with AI detecting an exposed debug endpoint, scanning the log output, and identifying sensitive fields such as emails, IP addresses, or token values.
Value
- Detects one of the most common and overlooked exposure risks
- Identifies internal data leakage through forgotten debug endpoints
- Flags sensitive fields including tokens, IDs, emails, and stack traces
- Runs safely and consistently across your entire attack surface
- Helps teams close misconfigurations before attackers find them
Closing Takeaway
Debug logs are not meant to be public. When they are exposed, they reveal exactly the kind of internal details that attackers rely on.
S4E Create with AI turns this difficult and easily forgotten task into continuous automated coverage that keeps sensitive data out of public reach.
🧰 Check It Yourself
Check the sample scan below or watch the video for a live walkthrough.
class Job(Task):
def run(self):
asset = http_or_https(asset=self.asset, _headers=self.headers, session=self.session)
self.output['detail'] = []
self.output['compact'] = []
self.output['video'] = [f"python3 debug_log_sensitive_data_scan.py {asset}"]
try:
response = self.session.get(f"{asset}/api/v1/logs", headers=self.headers, timeout=self.timeout, verify=False)
self.output['video'].append(f"Request: GET {asset}/api/v1/logs")
if response.status_code == 200:
content = response.text
sensitive_patterns = {
'user_id': r'\b(?:user_id|uid|userId)\b\s*[:=]\s*["\']?\d{1,12}["\']?',
'email': r'[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}',
'ip_address': r'\b(?:(?:25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)\.){3}(?:25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)\b',
'jwt_token': r'\beyJ[A-Za-z0-9_-]{10,}\.[A-Za-z0-9_-]{10,}\.[A-Za-z0-9_-]{10,}\b',
'api_token': r'\b(?:token|api[_-]?key|access[_-]?token)\b\s*[:=]\s*["\']?[A-Za-z0-9._\-]{16,}["\']?',
'error_details': r'\b(?:Traceback|stacktrace|FATAL|CRITICAL)\b[^\n]{0,200}',
'file_path': r'(?:/(?:home|var|etc|usr|opt|tmp)/[A-Za-z0-9._/-]+)|(?:[A-Za-z]:\\(?:[^\\/:*?"<>|\r\n]+\\)+[^\\/:*?"<>|\r\n]+)',
}
detected = []
for field, pattern in sensitive_patterns.items():
matches = re.findall(pattern, content, re.IGNORECASE)
if matches:
detected.append(field)
for match in matches:
self.output['detail'].append(f"{field}: {match}")
self.output['video'].append(f"{field}: {match}")
if detected:
self.output['compact'].append("Sensitive information found in logs.")
else:
self.output['detail'].append("No sensitive data detected in debug logs.")
self.output['video'].append("No sensitive data detected in debug logs.")
else:
self.output['detail'].append("Debug logs endpoint not found.")
self.output['video'].append("Debug logs endpoint not found.")
except:
self.output['detail'].append("Debug logs endpoint not found.")
self.output['video'].append("Debug logs endpoint not found.")
def calculate_score(self):
if self.output['compact']:
self.score = self.param['max_score']
else:
self.score = 0