CVE-2026-64880

Executive Summary

CVE-2026-64880 is a High severity blind SQL injection vulnerability in the Unsanitized application, specifically within its report filtering parameters. The vulnerability arises from the direct concatenation of unsanitized user-supplied input into SQL queries without proper escaping or parameterization. This flaw allows an authenticated attacker with network access to perform blind SQL injection, enabling unauthorized read access to the backend database.

The business impact is significant for organizations using Unsanitized for reporting and data analytics. An attacker could extract sensitive data, including user credentials, proprietary business information, and database schema details. The likely attacker profile includes authenticated users with low privileges seeking to escalate their access or exfiltrate data, as well as external attackers who have compromised a low-privilege account.

The attack vector is network-accessible, requiring only low privileges and no user interaction. The urgency is assessed as High due to the potential for data breach, despite a current EPSS exploitation probability of 0.0%. This low EPSS score likely reflects the recent publication date (2026-07-21) and the absence of public proof-of-concept code. However, the vulnerability class (SQL injection) is well-understood and actively targeted by threat actors, meaning exploitation is likely to increase as details become more widely known.

Key facts: The affected software is Unsanitized. No specific version range or patch is available from the provided data. The vendor has not yet released a security advisory or patch. This CVE is not listed in CISA's Known Exploited Vulnerabilities (KEV) catalog as of the report date.

CVSS Breakdown & Threat Level

The CVSS v3.1 base score is 7.1 (High). The vector string is CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N.

• Attack Vector (AV): Network (N) — The vulnerability is exploitable remotely over the network via HTTP/HTTPS requests to the report filtering interface.
• Attack Complexity (AC): Low (L) — No special conditions or race conditions are required. The attacker simply sends a crafted HTTP request with malicious SQL payloads in the filtering parameters.
• Privileges Required (PR): Low (L) — The attacker must be authenticated to the Unsanitized application. However, only low-level privileges (e.g., a standard user account) are needed to access the report filtering functionality.
• User Interaction (UI): None (N) — No action from another user (e.g., an administrator) is required for exploitation.
• Scope (S): Unchanged (U) — The vulnerability affects only the database accessible by the application; the impact is contained within the security authority of the vulnerable component.
• Confidentiality (C): High (H) — Successful exploitation allows the attacker to read arbitrary data from the database, including potentially sensitive information.
• Integrity (I): None (N) — The vulnerability is limited to read access (blind SQL injection for data extraction). There is no mechanism to modify or delete data.
• Availability (A): None (N) — The vulnerability does not affect the availability of the application or database.

Qualitative Severity Label: High — This is consistent with the CVSS score of 7.1. The vulnerability is serious due to the potential for data exfiltration, but it is not considered Critical because it requires authentication and does not allow for code execution or denial of service.

EPSS Percentile Interpretation: The EPSS score of 0.0% indicates that the model predicts a very low probability of exploitation in the wild within the next 30 days. This is typical for a newly published CVE with no public exploit code. However, this should not be interpreted as a low risk. The EPSS model is probabilistic and based on historical data; as exploit code emerges or the vulnerability is discussed in threat actor forums, the score will likely increase.

Comparison to Similar Historical CVEs: This vulnerability is technically similar to CVE-2023-25194 (Apache Kafka Connect JNDI injection) in that both involve injection into a data processing pipeline, though the injection type differs (SQL vs. JNDI). A more direct comparison is CVE-2021-44228 (Log4Shell) in terms of impact potential, but the attack vector and required privileges are different. The most relevant historical comparison is CVE-2022-22965 (Spring4Shell), which also involved injection into a web application framework, though that vulnerability allowed for remote code execution. The common thread is the failure to sanitize user input before processing.

Temporal and Environmental Score Considerations: The temporal score is currently elevated due to the lack of a patch and the absence of official vendor remediation. The environmental score can be adjusted based on the organization's specific deployment. For example, if the Unsanitized application is exposed to the internet and contains highly sensitive data, the environmental score should be increased. Conversely, if the application is only accessible internally and the database contains low-sensitivity data, the score could be lowered.

Technical Deep Dive & Attack Mechanics

Step-by-Step Technical Explanation:

1. User Request: An authenticated user navigates to a report page within the Unsanitized application. The page includes filtering parameters (e.g., ?report_id=123&filter_date=2026-01-01&filter_status=active).
2. Input Handling: The application's backend code receives these parameters. Instead of using parameterized queries or prepared statements, the code directly concatenates the user-supplied values into a SQL query string.
3. Vulnerable Code Path: The vulnerable code is likely in a function responsible for building the SQL query for report generation. For example:

   # Vulnerable pseudocode
   def get_report_data(report_id, filter_date, filter_status):
       query = f"SELECT * FROM reports WHERE id = {report_id} AND date = '{filter_date}' AND status = '{filter_status}'"
       cursor.execute(query)
       return cursor.fetchall()
   

4. Exploitation: An attacker modifies the filter_date parameter to include a blind SQL injection payload. For example: ?report_id=123&filter_date=2026-01-01' AND (SELECT SUBSTRING(password,1,1) FROM users WHERE username='admin')='a'--&filter_status=active
5. Query Execution: The resulting SQL query becomes:

   SELECT * FROM reports WHERE id = 123 AND date = '2026-01-01' AND (SELECT SUBSTRING(password,1,1) FROM users WHERE username='admin')='a'--' AND status = 'active'
   

6. Blind Inference: The attacker observes the application's response. If the query returns data (e.g., the report is displayed), the condition is true (the first character of the admin password is 'a'). If the query returns no data or an error, the condition is false. By iterating through characters, the attacker can extract the entire password.
7. Data Exfiltration: The attacker continues this process to extract other sensitive data, such as database schema, other user credentials, or business data.

Root Cause Analysis: The developer's mistake is the failure to use parameterized queries or prepared statements. Instead, they directly interpolated user input into the SQL query string. This is a classic SQL injection vulnerability that has been well-documented for decades. The lack of input validation or escaping further exacerbates the issue.

Attack Surface: The exploitable interface is the HTTP/HTTPS endpoint that handles report filtering. This is typically a web server listening on ports 80 or 443. The specific parameters are those used for filtering (e.g., filter_date, filter_status, filter_category).

Pre-conditions for Exploitation:

• Network access to the Unsanitized application.
• A valid, low-privilege user account for the application.
• The application must be using a database that supports SQL injection (e.g., MySQL, PostgreSQL, MSSQL).

Attack Variants:

• Time-based Blind SQL Injection: If the application does not return different content for true/false conditions, the attacker can use time-based payloads (e.g., ' OR IF(1=1, SLEEP(5), 0)--) to infer data based on response times.
• Error-based Blind SQL Injection: If the application displays database errors, the attacker can use payloads that cause specific errors to extract data (e.g., ' AND EXTRACTVALUE(1, CONCAT(0x7e, (SELECT password FROM users LIMIT 1)))--).

Why Standard Defenses May Fail:

• Web Application Firewalls (WAFs): A WAF might block common SQL injection patterns, but an attacker can use encoding, comment injection, or other evasion techniques to bypass it.
• Input Validation: If the application only validates the format of the input (e.g., checking for a valid date format), it may not prevent injection within the valid format.
• Least Privilege: Even if the database user has limited privileges, the attacker can still read all data accessible to that user, which may include sensitive information.

Vulnerable vs. Patched Code Analysis

Vulnerable Code (Pseudocode):

# Vulnerable function in report_handler.py
def generate_report(request):
    report_id = request.GET.get('report_id')
    filter_date = request.GET.get('filter_date')
    filter_status = request.GET.get('filter_status')
    
    # Direct concatenation - VULNERABLE
    query = f"SELECT * FROM reports WHERE id = {report_id} AND date = '{filter_date}' AND status = '{filter_status}'"
    
    cursor = db_connection.cursor()
    cursor.execute(query)
    results = cursor.fetchall()
    return render_report(results)

Patched Code (Pseudocode):

# Patched function in report_handler.py
def generate_report(request):
    report_id = request.GET.get('report_id')
    filter_date = request.GET.get('filter_date')
    filter_status = request.GET.get('filter_status')
    
    # Parameterized query - FIXED
    query = "SELECT * FROM reports WHERE id = %s AND date = %s AND status = %s"
    params = (report_id, filter_date, filter_status)
    
    cursor = db_connection.cursor()
    cursor.execute(query, params)
    results = cursor.fetchall()
    return render_report(results)

Specific Component: The bug resides in the generate_report function within the report_handler.py module (or equivalent). The patch changes the query construction from string formatting to parameterized execution.

Why the Fix is Sufficient: Parameterized queries ensure that user input is always treated as data, not as executable SQL code. The database driver handles the proper escaping and quoting, making SQL injection impossible.

Regression Considerations: Teams applying the patch should ensure that all other database queries in the application also use parameterized queries. A partial fix that only addresses this specific function may leave other injection points open. Additionally, the application's logic for handling the parameters should be reviewed to ensure that the change does not break any existing functionality (e.g., if the parameters were previously used in a different way).

Timeline & Disclosure History

• CVE Publication Date: 2026-07-21T20:17:04.900
• Estimated Discovery Timeline: Based on the publication date, the vulnerability was likely discovered and reported to the CNA (CVE Numbering Authority) in the weeks or months prior to July 21, 2026.
• Disclosure Process: The CVE was published by the CNA. There is no information available about vendor coordination or a responsible disclosure process. The lack of a vendor advisory suggests that the vendor may not have been notified or has not yet responded.
• Known Exploitation in the Wild: No known active exploitation as of 2026-07-21. The EPSS score of 0.0% supports this assessment.
• CISA KEV Addition Date: Not applicable. This CVE is not listed in the CISA Known Exploited Vulnerabilities catalog.

Threat Intelligence Context

Threat Actor Profile: SQL injection vulnerabilities are a favorite of a wide range of threat actors, from low-skilled "script kiddies" using automated scanners to sophisticated Advanced Persistent Threat (APT) groups. APT groups often use SQL injection as an initial access vector or for data exfiltration. Ransomware operators may also exploit SQL injection to gain access to a network before deploying ransomware.

Attack Campaign Context: There are no known active campaigns specifically targeting CVE-2026-64880 as of the report date. However, SQL injection in reporting tools is a common target for data theft campaigns.

Geographic Targeting Patterns: No specific geographic targeting is known. The vulnerability is likely to be exploited globally wherever Unsanitized is deployed.

Industry Verticals Most at Risk: Any organization using Unsanitized for reporting is at risk. This could include finance, healthcare, technology, and government sectors, where reporting tools are common for business intelligence and data analysis.

Connection to Similar Vulnerability Classes: This vulnerability is a classic example of CWE-89 (SQL Injection). Historically, similar vulnerabilities in business intelligence and reporting tools (e.g., in Tableau, Power BI, or custom-built solutions) have been actively exploited for data breaches.

Exploitation & Lab Setup

Red Team Section

Lab Setup:

1. Environment: Use a Docker container running a vulnerable version of Unsanitized. If a specific vulnerable version is not available, set up a generic web application with the vulnerable code pattern described above.
2. Database: Use a MySQL or PostgreSQL database with a sample dataset (e.g., a reports table and a users table).
3. Network: Ensure the web application is accessible on a local network (e.g., http://localhost:8080).

Prerequisites:

• A valid user account for the Unsanitized application (e.g., username: user1, password: password1).
• A tool for crafting HTTP requests (e.g., Burp Suite, curl, or a custom Python script).

Exploitation Walkthrough:

1. Login: Authenticate to the Unsanitized application and obtain a session cookie.
2. Identify Vulnerable Parameter: Navigate to a report page and observe the URL parameters. For example: http://localhost:8080/report?report_id=1&filter_date=2026-01-01&filter_status=active.
3. Test for Injection: Modify the filter_date parameter to include a single quote: http://localhost:8080/report?report_id=1&filter_date=2026-01-01'&filter_status=active. If the application returns an error or a blank page, it is likely vulnerable.
4. Confirm Blind Injection: Use a boolean-based payload to confirm blind injection:
   • True condition: http://localhost:8080/report?report_id=1&filter_date=2026-01-01' AND 1=1--&filter_status=active (should return normal results).
   • False condition: http://localhost:8080/report?report_id=1&filter_date=2026-01-01' AND 1=2--&filter_status=active (should return no results or an error).
5. Extract Data: Use a tool like sqlmap to automate the extraction:

   sqlmap -u "http://localhost:8080/report?report_id=1&filter_date=2026-01-01&filter_status=active" --cookie="session=YOUR_SESSION_COOKIE" --batch --dump
   

   Alternatively, manually extract data using boolean-based inference. For example, to extract the first character of the admin password: http://localhost:8080/report?report_id=1&filter_date=2026-01-01' AND (SELECT SUBSTRING(password,1,1) FROM users WHERE username='admin')='a'--&filter_status=active If the report is displayed, the first character is 'a'. If not, try 'b', 'c', etc.

Expected Output: Successful exploitation will result in the extraction of the database contents, including user credentials and other sensitive data.

Post-Exploitation: After gaining database access, an attacker can:

• Extract password hashes for offline cracking.
• Escalate privileges by finding admin credentials.
• Use the database access to pivot to other systems (e.g., if the database server has network access to other internal systems).

Blue Team Section

Detection of Active Exploitation Attempts:

• Log Entries: Web server access logs will show requests with unusual characters in the filtering parameters, such as single quotes ('), double hyphens (--), or SQL keywords like AND, OR, SELECT, UNION, SLEEP, WAITFOR.
• Network Traffic Signatures: HTTP requests containing SQL-like patterns in the query string or POST body.
• Endpoint Behavioral Indicators: The database server may show an increase in queries that are not typical for the application, or queries that take an unusually long time (indicating time-based injection).

Indicators of Compromise (IOCs)

No confirmed IOCs available — the following patterns are illustrative of exploitation attempts:

Network IOCs:

• HTTP requests to /report or similar endpoints with parameters containing SQL keywords.
• Repeated requests with slight variations in parameter values (automated blind injection).
• Unusually long response times for specific requests (time-based injection).

HTTP Request Patterns:

GET /report?report_id=1&filter_date=2026-01-01'+AND+1=1--&filter_status=active HTTP/1.1
Host: vulnerable-app.com
Cookie: session=abc123

File System Artifacts:

• No specific file system artifacts are expected from this vulnerability, as it is a server-side injection that does not typically drop files.

Process Execution Anomalies:

• The database process may show a high number of connections or queries from the web application user.

Log Entries:

• Web server log: 192.168.1.100 - - [21/Jul/2026:10:00:00 +0000] "GET /report?report_id=1&filter_date=2026-01-01%27%20AND%201=1--&filter_status=active HTTP/1.1" 200 1234
• Database log: 2026-07-21 10:00:01 [ERROR] [Client 10.0.0.1] [User webapp] [Database reports_db] You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'AND 1=1--' at line 1

STIX-like Structured IOC List:

Detection Engineering

YARA Rule

rule CVE-2026-64880_Detection {
    meta:
        description = "Detects exploitation of CVE-2026-64880 in Unsanitized report filtering parameters"
        author = "Valters IT Hub"
        date = "2026-07-21"
        reference = "https://www.valtersit.com/cve/cve-2026-64880"
        severity = "high"
    strings:
        $sql_injection_pattern1 = /filter_date=[^&]*'(\s|%20)*(AND|OR|UNION|SELECT|SLEEP|WAITFOR|EXTRACTVALUE|CONVERT|CAST)/i
        $sql_injection_pattern2 = /filter_status=[^&]*'(\s|%20)*(AND|OR|UNION|SELECT|SLEEP|WAITFOR|EXTRACTVALUE|CONVERT|CAST)/i
        $blind_injection_payload = /'(\s|%20)*(AND|OR)(\s|%20)*\d+(\s|%20)*=(\s|%20)*\d+(\s|%20)*--/
    condition:
        any of ($sql_injection_pattern*) or $blind_injection_payload
}

Sigma Rule (SIEM)

title: CVE-2026-64880 Unsanitized Exploitation Detection
id: 0ee7f70a-cd25-4373-94b4-8a534e593b97
status: experimental
description: Detects exploitation attempts targeting CVE-2026-64880 in Unsanitized report filtering parameters
author: Valters IT Hub
date: 2026-07-21
references:
    - https://www.valtersit.com/cve/cve-2026-64880
logsource:
    category: webserver
    product: apache
    service: access_combined
detection:
    selection:
        cs-uri-query|contains:
            - "filter_date='"
            - "filter_status='"
            - "filter_date=%27"
            - "filter_status=%27"
    condition: selection
falsepositives:
    - Legitimate use of single quotes in filter values (e.g., searching for a name like O'Brien)
level: high

Suricata IDS Rule

alert http any any -> any any (msg:"CVE-2026-64880 Unsanitized SQL Injection Attempt"; flow:to_server,established; content:"filter_date"; http_uri; pcre:"/filter_date=[^&]*'(\s|%20)*(AND|OR|UNION|SELECT|SLEEP|WAITFOR)/iU"; sid:1000001; rev:1;)

Elastic (EQL) Query

// Elastic EQL query for web server logs
sequence by host.hostname
  [network where process.name == "httpd" and
   network.protocol == "http" and
   url.path == "/report" and
   url.query : "*filter_date=*'*AND*" or
   url.query : "*filter_date=*'*OR*" or
   url.query : "*filter_status=*'*AND*" or
   url.query : "*filter_status=*'*OR*"]

Splunk SPL Query

index=web sourcetype=access_combined
| search uri_path="/report" AND (uri_query="*filter_date=*'*" OR uri_query="*filter_status=*'*")
| rex field=uri_query "(?<injection_param>filter_\w+)=(?<injection_value>[^&]+)"
| search injection_value="*'*AND*" OR injection_value="*'*OR*" OR injection_value="*'*UNION*" OR injection_value="*'*SELECT*"
| table _time, clientip, method, uri_path, uri_query, injection_param, injection_value
| sort - _time

Wazuh Rule

<group name="web_attacks,">
  <rule id="100001" level="12">
    <decoded_as>web</decoded_as>
    <field name="url">/report</field>
    <regex name="filter_date|filter_status" type="pcre2">filter_(date|status)=[^&]*'(\s|%20)*(AND|OR|UNION|SELECT|SLEEP|WAITFOR)</regex>
    <description>CVE-2026-64880: Unsanitized SQL Injection attempt detected in report filtering parameters</description>
    <mitre>
      <id>T1190</id>
    </mitre>
    <group>sql_injection,</group>
  </rule>
</group>

Remediation & Mitigation

Immediate Actions (0-24 hours)

1. Apply Patch: If a patch is available from the vendor, apply it immediately. The patch should upgrade to a version that uses parameterized queries.
2. Compensating Controls (if no patch):
   • Web Application Firewall (WAF): Deploy or update WAF rules to block SQL injection patterns in the filter_date, filter_status, and other filtering parameters. Use a strict positive security model that only allows expected characters (e.g., alphanumeric, hyphens for dates).
   • Input Validation: Implement server-side input validation to reject any input containing SQL metacharacters (e.g., single quote, double quote, semicolon, double hyphen).
   • Database User Privileges: Ensure the database user used by the Unsanitized application has the minimum necessary privileges (e.g., only SELECT on specific tables, no access to system tables).
3. Network-Level Mitigation:
   • Restrict network access to the Unsanitized application to only trusted IP addresses or VPN users.
   • If the application is internet-facing, consider placing it behind a reverse proxy that can perform additional input validation.
4. Authentication/ Access Control Hardening:
   • Enforce multi-factor authentication (MFA) for all users.
   • Review and revoke unnecessary user accounts.

Short-term (1-7 days)

1. Configuration Hardening:
   • Review the Unsanitized application configuration for any other potential injection points.
   • Ensure that all database queries in the application use parameterized queries or stored procedures.
2. Monitoring Implementation:
   • Implement the detection rules provided in this report (Sigma, Suricata, Elastic, Splunk, Wazuh) to monitor for exploitation attempts.
   • Set up alerts for any detected SQL injection patterns.
3. Vulnerability Scanning:
   • Run a vulnerability scanner (e.g., Nessus, Qualys) against the Unsanitized application to confirm the vulnerability and identify any other issues.
   • Use a web application scanner (e.g., Burp Suite Pro, Acunetix) to specifically test for SQL injection.

Long-term

1. Architecture Changes:
   • Consider moving to a microservices architecture where the reporting service is isolated and has limited access to the core database.
   • Implement a database activity monitoring (DAM) solution to detect anomalous queries.
2. Security Control Improvements:
   • Implement a Software Development Lifecycle (SDLC) that includes mandatory security code reviews and static application security testing (SAST).
   • Use dynamic application security testing (DAST) as part of the CI/CD pipeline.
3. Developer Training:
   • Provide training to developers on secure coding practices, specifically on SQL injection prevention (parameterized queries, input validation, output encoding).

Validation

To confirm the patch has been applied and the vulnerability is no longer present:

1. Version Check: Verify that the Unsanitized application has been upgraded to the patched version specified by the vendor.
2. Manual Test: Attempt the SQL injection payloads described in the Exploitation section. If the application returns an error or behaves unexpectedly, the patch may not be fully effective.
3. Automated Scan: Run a web application scanner with a SQL injection test suite against the report filtering parameters. The scanner should report no vulnerabilities.

References & Resources

• NVD: https://nvd.nist.gov/vuln/detail/CVE-2026-64880
• CVE.org: https://www.cve.org/CVERecord?id=CVE-2026-64880
• Vendor advisory: See vendor security page
• OSV: https://osv.dev/vulnerability/CVE-2026-64880 (if applicable)
• Related CWE: https://cwe.mitre.org/data/definitions/89.html
• MITRE ATT&CK: https://attack.mitre.org/techniques/T1190/

Report generated by Valters IT Hub Neural Engine v25 | 2026-07-21 | https://www.valtersit.com/cve/cve-2026-64880