Attackers are bypassing MFA on SonicWall VPNs because something was wrong with previous fix


Attackers are bypassing MFA on SonicWall VPNs because something was wrong with previous fix

Pierluigi Paganini
May 21, 2026

Attackers bypassed MFA on patched SonicWall Gen6 VPNs because admins missed extra manual steps required to fully fix the flaw.

There is a particular kind of security failure that is harder to catch than an unpatched system: a patched system where the patch did not actually work because nobody followed all the steps. That is what is happening right now with SonicWall Gen6 SSL-VPN appliances and CVE-2024-12802, and it has already led to ransomware-related intrusions across multiple organizations.

Between February and March 2026, ReliaQuest researchers observed what it assesses as the first in-the-wild exploitation of CVE-2024-12802 across multiple environments. The flaw is an authentication bypass in SonicWall VPNs that can reduce security to single-factor access. Although firmware updates exist for Gen6 devices, full remediation requires six additional manual steps, often missed in standard patching workflows, leaving systems exposed despite appearing fixed. Attackers then brute-forced VPN accounts, bypassed MFA, and rapidly moved inside networks, sometimes reaching file servers in under 30 minutes.

“In the intrusions we observed, threat actors brute-forced VPN accounts and bypassed MFA to gain access to internal networks. The tools observed were consistent with actors operating in the ransomware ecosystem. In some cases, as few as 13 brute-force attempts separated an attacker from a valid credential. In one environment, they reached a file server within 30 minutes and deployed tools consistent with pre-ransomware staging.” reads the post by ReliaQuest. “Intrusions left the same signal in the logs: A session type associated with automated VPN authentication that most organizations are unlikely to be monitoring today.”

SonicWall

The vulnerability itself, CVE-2024-12802, is stems in how SonicWall handles two different Active Directory login formats: UPN (User Principal Name, the format that looks like an email address) and SAM (Security Account Manager, the older format). MFA enforcement is applied to each login format independently, not to the user identity behind them. An attacker who knows valid credentials can authenticate using the UPN path even when MFA is configured, because the enforcement for that specific path is missing.

“Patching the firmware doesn’t remove the existing Lightweight Directory Access Protocol (LDAP) configuration that allows the bypass; the vulnerable configuration remains in place.” continues ReliaQuest. “Remediation requires deleting that configuration entirely and rebuilding it without the userPrincipalName format that the exploit relies on. SonicWall’s advisory (SNWLID-2025-0001) specifies six additional manual steps.

SonicWall documented all of this in their advisory, but standard patch management workflows are not designed to verify manual reconfiguration steps, the firmware updates, the version check passes, and the device looks fine.

“SSL-VPN MFA Bypass in SonicWALL SSL-VPN can arise in specific cases due to the separate handling of UPN (User Principal Name) and SAM (Security Account Manager) account names when integrated with Microsoft Active Directory, allowing MFA to be configured independently for each login method and potentially enabling attackers to bypass MFA by exploiting the alternative account name.” reads the advisory. “IMPORTANT: For GEN7 and GEN8 Firewalls, we have incorporated the remediation steps described in the advisory (Comments section) into versions 7.2.0-7015 and 8.0.1-8017. These versions also include additional security enhancements. After upgrading the firewall to the specified version, the use of userPrincipalName in LDAP server configurations is once again supported.

For Gen7 and Gen8 devices, this is not a problem. A firmware update is enough. The issue is specific to Gen6 hardware, which also hit end-of-life on April 16 this year and no longer receives security updates — a detail that makes the remediation conversation even more urgent.

The intrusion pattern ReliaQuest observed was consistent across multiple incidents. In one incident, the attacker went from initial VPN access to reaching a domain-joined file server and establishing an RDP connection using a shared local administrator password in under thirty minutes.

The behavior after that initial foothold was telling: the attacker attempted to deploy a Cobalt Strike beacon for command-and-control and tried to load a vulnerable driver, likely to kill endpoint protection using the Bring Your Own Vulnerable Driver technique. The EDR on that particular system blocked both. But the attacker logged out deliberately, came back days later using different accounts, and repeated the pattern, behavior more consistent with an initial access broker assessing victim value than with a ransomware group executing immediately.

One detail that made detection particularly difficult, as said in the report.

“The rogue login attempts observed in the investigated incidents still appeared as a normal MFA flow in logs, leading defenders to believe that MFA worked even when it failed. The sess=’CLI’ signal is a key indicator of these attacks, which suggests scripted or automated VPN authentication.” continues the report.

In other words, the logs showed what looked like successful MFA authentication, giving security teams no obvious signal that anything was wrong.

Event IDs 238 and 1080 are additional indicators worth monitoring, along with VPN logins originating from VPS or VPN infrastructure rather than expected user locations.

If you are running Gen6 SonicWall SSL-VPN appliances, confirm that the full remediation has been completed, not just that the firmware is current. The six-step LDAP reconfiguration process in SonicWall’s advisory is the actual fix. Firmware version alone is not sufficient to confirm you are protected.

The broader recommendation for Gen6 hardware is migration to a supported generation, given that end-of-life status means no future security patches. That is a bigger project, but the alternative is running perimeter infrastructure that will not receive fixes for whatever comes next.

For defenders reviewing logs right now, the sess=”CLI” indicator in VPN authentication logs is worth a search against recent history. If you find it alongside successful authentications for accounts that should be MFA-protected, you have a problem that predates this advisory.

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, SonicWall VPNs)







Source link

Leave a Reply

Subscribe to Our Newsletter

Get our latest articles delivered straight to your inbox. No spam, we promise.

Recent Reviews


datafoundationgettyimages-1472653690

Eugene Mymrin/ Moment via Getty Images

Follow ZDNET: Add us as a preferred source on Google.


ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





Source link