CVE-2026-34197
Improper Input Validation in Improper Input Validation, Improper Control of Generation of Code ('Code Injection') vulnerability in Apache ActiveMQ Broker, Apache ActiveMQ
Executive Summary
CVE-2026-34197 is a high severity vulnerability affecting appsec. It is classified as CWE-20. This vulnerability is actively being exploited in the wild.
Precogs AI Insight
"Apache ActiveMQ processes serialized data structures without enforcing strict type constraints, leading to improper input validation. Adversaries exploit this by sending maliciously crafted serialized objects that trigger code injection upon deserialization within the broker. Precogs AI Analysis Engine tracks unsafe deserialization patterns via advanced Code Property Graph traversal."
What is this vulnerability?
CVE-2026-34197 is categorized as a high Improper Input Validation flaw with a CVSS base score of 8.8. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.
Improper Input Validation, Improper Control of Generation of Code ('Code Injection') vulnerability in Apache ActiveMQ Broker, Apache ActiveMQ.
Apache ActiveMQ Classic exposes the Jolokia JMX-HTTP bridge at /api/jolokia/ on the web console. The default Jolokia access policy permits exec operations on all ActiveMQ MBeans (org.apache.activemq:*), including BrokerService.addNetworkConnector(String) and BrokerService.addConnector(String).
An authenticated attacker can invoke these operations with a crafted discovery URI that triggers the VM transport's brokerConfig parameter to load a remote Spring XML application context using ResourceXmlApplicationContext. Because Spring's ResourceXmlApplicationContext instantiates all singleton beans before the BrokerService validates the configuration, arbitrary code execution occurs on the broker's JVM through bean factory methods such as Runtime.exec().
This issue affects Apache ActiveMQ Broker: before 5.19.4, from 6.0.0 before 6.2.3; Apache ActiveMQ All: before 5.19.4, from 6.0.0 before 6.2.3; Apache ActiveMQ: before 5.19.4, from 6.0.0 before 6.2.3.
Users are recommended to upgrade to version 5.19.4 or 6.2.3, which fixes the issue
This architectural defect enables adversaries to bypass intended security controls, directly manipulating the application's execution state or data layer. Immediate strategic intervention is required.
Risk Assessment
| Metric | Value |
|---|---|
| CVSS Base Score | 8.8 (HIGH) |
| Vector String | CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H |
| Published | April 7, 2026 |
| Last Modified | April 16, 2026 |
| Related CWEs | CWE-20, CWE-94 |
Impact on Systems
✅ Data Exfiltration: Attackers can extract sensitive data from backend databases, configuration files, or internal services.
✅ Authentication Bypass: Exploiting this flaw may allow unauthorized access to protected resources and administrative interfaces.
✅ Lateral Movement: Once initial access is gained, attackers can pivot to internal systems and escalate privileges.
How to Fix and Mitigate CVE-2026-34197
- Apply Vendor Patches Immediately: This vulnerability is listed in CISA's Known Exploited Vulnerabilities catalog. Apply mitigations per vendor instructions, follow applicable BOD 22-01 guidance for cloud services, or discontinue use of the product if mitigations are unavailable.
- Verify Patch Deployment: Confirm all instances are updated using Precogs continuous monitoring.
- Review Audit Logs: Investigate historical access logs for indicators of compromise related to this attack surface.
- Implement Defense-in-Depth: Deploy WAF rules, network segmentation, and endpoint detection to limit blast radius.
Defending with Precogs AI
Precogs AI Analysis Engine identifies this vulnerability class through semantic code analysis powered by Code Property Graph (CPG) technology, performing inter-procedural taint tracking to detect injection flaws, broken authentication, and insecure data flows across your entire codebase.
Use Precogs to continuously scan your codebase, binaries, APIs, and infrastructure for this vulnerability class and related attack patterns. Our AI-powered detection engine combines static analysis with threat intelligence to identify exploitable weaknesses before attackers do.
Vulnerability Code Signature
Attack Data Flow
| Stage | Detail |
|---|---|
| Source | Untrusted User Input |
| Vector | Input flows through the application logic without sanitization |
| Sink | Execution or Rendering Sink |
| Impact | Application compromise, Logic Bypass, Data Exfiltration |
Vulnerable Code Pattern
# ❌ VULNERABLE: Unsanitized Input Flow
def process_request(request):
user_input = request.GET.get('data')
# Taint sink: processing untrusted data
execute_logic(user_input)
return {"status": "success"}
Secure Code Pattern
# ✅ SECURE: Input Validation & Sanitization
def process_request(request):
user_input = request.GET.get('data')
# Sanitized boundary check
if not is_valid_format(user_input):
raise ValueError("Invalid input format")
sanitized_data = sanitize(user_input)
execute_logic(sanitized_data)
return {"status": "success"}
How Precogs Detects This
Precogs AI Analysis Engine maps untrusted input directly to execution sinks to catch complex application security vulnerabilities.\n