CVE-2026-20128

What is this vulnerability?

CVE-2026-20128 is categorized as a high CWE-257 flaw with a CVSS base score of 7.5. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

A vulnerability in the Data Collection Agent (DCA) feature of Cisco Catalyst SD-WAN Manager could allow an unauthenticated, remote attacker to gain DCA user privileges on an affected system.

This vulnerability is due to the presence of a credential file for the DCA user on an affected system. An attacker could exploit this vulnerability by sending a crafted HTTP request and reading the file that contains the DCA password from that affected system. A successful exploit could allow the attacker to access another affected system and gain DCA user privileges. Note: Cisco Catalyst SD-WAN Manager releases 20.18 and later are not affected by this vulnerability.

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

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-2026-20128

1. Apply Vendor Patches Immediately: This vulnerability is listed in CISA's Known Exploited Vulnerabilities catalog. Please adhere to CISA’s guidelines to assess exposure and mitigate risks associated with Cisco SD-WAN devices as outlines in CISA’s Emergency Directive 26-03 (URL listed below in Notes) and CISA’s “Hunt & Hardening Guidance for Cisco SD-WAN Devices (URL listed below in Notes). Adhere to the applicable BOD 22-01 guidance for cloud services or discontinue use of the product if mitigations are not available.
2. Verify Patch Deployment: Confirm all instances are updated using Precogs continuous monitoring.
3. Review Audit Logs: Investigate historical access logs for indicators of compromise related to this attack surface.
4. 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.

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Vulnerability Code Signature

Attack Data Flow

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