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CVSS: 8.8 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H 🐛 CWE-564
👁️
Common Weakness Enumeration - Identifies the specific category of software weakness or flaw.

CVE-2025-8052

CVE-2025-8052 SQL Injection in Opentext
📅 PUBLISHED: 2025-10-20 19:57 UTC 🔄 UPDATED: 2026-10-09 10:28 UTC
[ PATCH UNDER REVIEW ↓ ] Remains UNDER REVIEW until an independent source confirms the fix
[ DEFENSE PATCH ↓ ]
💣 PoC: None
👁️
Proof of Concept - Indicates if functional exploit code is publicly available, drastically increasing the risk of immediate attacks.
🎯 EPSS: 0.36%
👁️
0.36% probability
8.9× above the median CVE
Tier: ELEVATED
30-day exploitation window · FIRST.org · updated daily
VULNERABLE: version not auto-detected — verify your deployed version locally
CVSS VECTOR BREAKDOWN 8.8
AV: N Attack Vector Network
AC: L Attack Complexity Low
PR: L Privileges Required Low
UI: N User Interaction None
S: U Scope Unchanged
C: H Confidentiality High
I: H Integrity High
A: H Availability High
8.8
THREAT LEVEL
■ tracking this CVE ✓ independent patch confirmation ⧗ same-origin mirror (GHSA) ⚠ active exploitation verification policy →

Executive Summary

CVE-2025-8052 is a SQL Injection vulnerability discovered in OpenText Flipper version 3.1.2. The flaw allows a low-privilege user to interact with the database in unintended ways by manipulating the HQL (Hibernate Query Language) processor. This can lead to unauthorized data extraction, modification, or deletion. No public proof-of-concept (PoC) or patch is currently available, and the CVSS score is listed as 0.0, indicating incomplete assessment. The vulnerability is classified under CWE-89 (SQL Injection) and aligns with MITRE ATT&CK tactic T1190 (Exploit Public-Facing Application).

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Supply Chain & Package Security

This vulnerability is not related to NPM, PyPI, Go, or Ruby ecosystems. It affects a proprietary OpenText product (Flipper) and is not part of an open-source package registry.

CVSS Breakdown & Threat Level

The CVSS score is listed as 0.0, which likely indicates that the vector has not been fully calculated or published. Based on the description (low-privilege user, network-based attack, SQL injection), a reasonable estimated vector would be:

  • Attack Vector (AV): Network (N)
  • Attack Complexity (AC): Low (L)
  • Privileges Required (PR): Low (L)
  • User Interaction (UI): None (N)
  • Scope (S): Unchanged (U)
  • Confidentiality (C): High (H)
  • Integrity (I): High (H)
  • Availability (A): High (H)

Estimated CVSS v3.1 Score: 8.8 (High)

Threat Level: High – Exploitation could lead to full database compromise, data exfiltration, and potential lateral movement.

Deep Dive & Mechanics

The vulnerability resides in the HQL processor of OpenText Flipper 3.1.2. HQL (Hibernate Query Language) is an object-oriented query language similar to SQL but used with Hibernate ORM. The application fails to properly sanitize user input when constructing HQL queries, allowing an attacker to inject malicious HQL statements.

Attack Flow:

  1. An attacker with low-privilege access (e.g., a standard user account) identifies an input field that is used in an HQL query.
  2. The attacker crafts a malicious HQL payload, such as:
    ' OR 1=1 --
    
    or more advanced payloads to extract data:
    ' UNION SELECT user, password FROM users --
    
  3. The application passes this input directly to the HQL processor without sanitization.
  4. The HQL processor executes the injected query, returning unauthorized data or allowing modification.

Why HQL Injection is Dangerous:

  • HQL can execute arbitrary database operations.
  • It bypasses some traditional SQL injection filters because it uses object-oriented syntax.
  • The attacker can enumerate tables, columns, and data.

Vulnerable vs. Patched Code

Since no patch is available, we can only describe the vulnerable pattern and the expected fix.

Vulnerable Code (Conceptual):

String userInput = request.getParameter("search");
String hql = "FROM Product WHERE name = '" + userInput + "'";
Query query = session.createQuery(hql);
List results = query.list();

Patched Code (Expected):

String userInput = request.getParameter("search");
String hql = "FROM Product WHERE name = :name";
Query query = session.createQuery(hql);
query.setParameter("name", userInput);
List results = query.list();

The fix uses parameterized queries (named parameters) to prevent injection.

Exploitation & Lab Setup

Red Team (Offensive)

Prerequisites:

  • Access to OpenText Flipper 3.1.2 with a low-privilege account.
  • A web browser or tool like Burp Suite.

Steps:

  1. Log in with low-privilege credentials.
  2. Identify an input field that interacts with the database (e.g., search, filter, or sort).
  3. Intercept the request with Burp Suite.
  4. Inject a test payload:
    ' OR '1'='1
    
  5. If the application returns unexpected data, confirm SQL injection.
  6. Extract data using UNION-based injection:
    ' UNION SELECT table_name, column_name FROM information_schema.columns --
    
  7. Use automated tools like sqlmap:
    sqlmap -u "http://target.com/vulnerable_endpoint?param=value" --cookie="session=..." --level=5 --risk=3
    

Example Payloads:

  • Boolean-based: ' AND 1=1 --
  • Time-based: ' OR SLEEP(5) --
  • UNION-based: ' UNION SELECT null, null, database() --

Blue Team (Defensive)

Detection:

  • Monitor for unusual HQL query patterns in application logs.
  • Look for characters like ', --, UNION, SLEEP in input fields.
  • Use WAF rules to block SQL/HQL injection patterns.

Hunting:

  • Search for requests with ' or OR in parameters.
  • Check for abnormal database response times (time-based injection).
  • Review database logs for unexpected queries.

Lab Setup:

  • Deploy OpenText Flipper 3.1.2 in an isolated environment.
  • Use a test database with dummy data.
  • Simulate low-privilege user access.
  • Attempt injection with safe payloads (e.g., ' OR '1'='1).

Indicators of Compromise (IOCs)

  • Network:
    • HTTP requests containing SQL/HQL injection patterns (e.g., ' OR, UNION SELECT, SLEEP).
    • Unusual database response sizes or times.
  • Host:
    • Application logs showing HQL errors or unexpected queries.
    • Database logs showing queries from low-privilege users that access restricted tables.
  • File:
    • Temporary files created by sqlmap or other tools.
  • Registry/Process:
    • Unusual database connections from the application server.

Detection Engineering

YARA Rule

rule CVE_2025_8052_HQL_Injection {
    meta:
        description = "Detects HQL injection attempts in web requests"
        author = "Valters IT"
        date = "2025-04-10"
        reference = "CVE-2025-8052"
    strings:
        $hql1 = /' OR\s+\d+=\d+/ nocase
        $hql2 = /' UNION\s+SELECT/ nocase
        $hql3 = /' AND\s+\d+=\d+/ nocase
        $hql4 = /SLEEP\(\d+\)/ nocase
    condition:
        any of them
}

Sigma Rule

title: HQL Injection Attempt Detection
id: 7a8b9c0d-1e2f-3a4b-5c6d-7e8f9a0b1c2d
status: experimental
description: Detects HQL injection patterns in HTTP requests
author: Valters IT
logsource:
    category: webserver
    product: apache
detection:
    selection:
        cs-uri-query|contains:
            - "' OR "
            - "' UNION "
            - "' AND "
            - "SLEEP("
    condition: selection
falsepositives:
    - Legitimate use of apostrophes in search terms
level: high

Suricata Rule

alert http any any -> any any (msg:"CVE-2025-8052 HQL Injection Attempt"; flow:to_server,established; content:"' OR "; nocase; content:"' UNION "; nocase; distance:0; content:"' AND "; nocase; distance:0; content:"SLEEP("; nocase; distance:0; sid:1000001; rev:1;)

Remediation & Mitigation

Immediate Actions:

  1. Isolate the affected Flipper instance from untrusted networks.
  2. Restrict low-privilege user permissions to the minimum required.
  3. Monitor logs for suspicious HQL queries.

Long-term Fix:

  • Apply parameterized queries (prepared statements) in all HQL interactions.
  • Upgrade to a patched version when available (contact OpenText support).
  • Implement input validation and output encoding.

Compensating Controls:

  • Deploy a Web Application Firewall (WAF) with SQL injection rules.
  • Use database activity monitoring to detect anomalous queries.
  • Conduct regular security audits and penetration testing.

Valters IT Signature

This report was generated by Valters IT Cyber Threat Intelligence Division.

◈ SOURCE Analysis & enrichment: valtersit.com/cve/CVE-2025-8052/ · CVE data: NVD/MITRE (public domain) · © 2026 ValtersIT Hub
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