Cybersecurity defenders are facing a new and dangerous evolution in threat actor tradecraft. Hackers are now weaponizing large language models (LLMs) — including DeepSeek and other AI platforms — to accelerate and refine attacks against FortiGate firewall infrastructure worldwide. According to recent threat intelligence reporting (CISA, 2025), over 150,000 FortiGate devices remain exposed to known vulnerabilities, and AI-assisted exploitation is dramatically lowering the barrier for even moderately skilled attackers.
FortiGate devices sit at the perimeter of enterprise networks, protecting critical infrastructure, cloud environments, and sensitive data. When threat actors combine AI-generated payloads with automated reconnaissance, these defenses can fall faster than traditional response timelines allow. The stakes extend across industries — from healthcare and finance to energy and government.
This article explains how AI tools are being operationalized in modern attack chains, what specific techniques threat actors are using against FortiGate infrastructure, and what your security team can do right now to reduce exposure.
How Threat Actors Are Weaponizing AI Against Network Infrastructure
The use of AI in offensive security is not theoretical. Security researchers and threat intelligence teams have documented active campaigns where adversaries use LLMs to compress the time between vulnerability discovery and exploitation.
AI-Generated Payload Development
AI models can generate syntactically valid exploit code, custom shellcode, and tailored malicious scripts in minutes. Threat actors prompt these models with vulnerability details — such as CVE descriptions or Proof-of-Concept (PoC) code fragments — and receive functional exploit variations tuned to specific firmware versions.
For FortiGate specifically, attackers have been observed targeting vulnerabilities in the SSL-VPN interface, where unauthenticated remote code execution (RCE) remains possible on unpatched devices. AI dramatically accelerates the mutation of known PoC code into evasion-ready payloads.
Evasion Technique Refinement
Beyond payload generation, AI assists in crafting network traffic patterns and encoding schemes that bypass signature-based detection. Threat actors use AI to:
- Rewrite exploit traffic to avoid known Intrusion Detection System (IDS) signatures
- Generate obfuscated command-and-control (C2) communication patterns
- Simulate legitimate administrative API calls to blend with normal FortiGate management traffic
- Produce Living-off-the-Land (LotL) techniques customized to FortiOS environments
Pro Tip: If your SOC relies heavily on signature-based detection alone, AI-obfuscated traffic will likely slip through. Behavioral analytics and anomaly detection become critical compensating controls.
The FortiGate Attack Chain: Reconnaissance to Initial Access
Understanding the full attack chain helps defenders identify where to intervene most effectively. MITRE ATT&CK framework tactics map clearly onto these AI-assisted campaigns.
Automated Reconnaissance at Scale
Attackers begin by scanning the internet for exposed FortiGate management interfaces, primarily on TCP ports 443 and 8443. Tools like Shodan and Censys aggregate this data publicly, and AI models help threat actors parse and prioritize targets based on firmware version fingerprinting.
Vulnerable instances — particularly those running FortiOS versions affected by CVE-2022-42475, CVE-2023-27997, and CVE-2024-21762 — are flagged automatically. No authentication is required to trigger these vulnerabilities, which makes mass exploitation trivially scalable.
Table: High-Impact FortiGate Vulnerabilities Targeted in AI-Assisted Campaigns
| CVE | CVSS Score | Attack Vector | Auth Required | Impact |
|---|---|---|---|---|
| CVE-2022-42475 | 9.3 | Network | None | RCE via SSL-VPN heap overflow |
| CVE-2023-27997 | 9.8 | Network | None | Pre-auth RCE in SSL-VPN |
| CVE-2024-21762 | 9.6 | Network | None | Out-of-bounds write, code exec |
Initial Access and Persistence
Once access is achieved, threat actors deploy lightweight implants or modify FortiOS configuration files to establish persistence. In documented incidents, attackers have added rogue administrative accounts, exfiltrated VPN credentials, and pivoted laterally into enterprise Active Directory environments — all within hours of initial compromise.
Indicators of Compromise Specific to AI-Assisted Attacks
Identifying AI-assisted intrusions requires looking beyond traditional IOC matching. The behavioral footprint of these attacks is distinct.
Anomalous API and Network Patterns
One of the more telling signals is unusual outbound API traffic originating from compromised infrastructure. Attackers routing requests through AI services — or using AI-generated code that phones home to cloud-based endpoints — leave detectable patterns.
Watch for:
- Outbound HTTPS connections to unfamiliar cloud API endpoints from firewall management interfaces
- Abnormally high volumes of authentication attempts with syntactically varied credentials (a sign of AI-generated password mutation)
- FortiOS log entries showing configuration changes outside maintenance windows
- Unexpected administrative sessions originating from non-RFC1918 IP addresses
Host and Log-Based Indicators
Table: Key IOCs for AI-Assisted FortiGate Compromise
| Indicator Type | Example Signal | Severity |
|---|---|---|
| Network | Outbound calls to AI API endpoints | High |
| Authentication | Rapid credential variation attempts | High |
| Configuration | New admin accounts added silently | Critical |
| Log anomaly | SSL-VPN session from unknown geo | Medium |
| File system | Modified FortiOS binaries or configs | Critical |
Important: A single indicator rarely confirms compromise. Correlate across log sources — FortiAnalyzer, SIEM ingestion, and endpoint telemetry — before concluding on incident severity.
Mitigation and Hardening: Immediate and Strategic Steps
The good news is that most AI-assisted attacks against FortiGate devices still depend on known, patchable vulnerabilities. Strong patch management discipline cuts the attack surface dramatically.
Urgent Patching and Configuration Hardening
Your first priority is eliminating the unauthenticated attack surface. Fortinet releases security advisories through its PSIRT portal, and patches for the critical CVEs listed above are available.
Immediate actions your team should take:
- Audit all internet-exposed FortiGate instances and confirm current FortiOS versions
- Apply patches for all CVEs rated 9.0 or above as an emergency change
- Disable SSL-VPN if it is not operationally required
- Restrict management interface access to trusted IP ranges only
- Enable multi-factor authentication (MFA) on all administrative accounts
Detection and Monitoring Enhancements
Patching alone is insufficient. Hardened monitoring posture is essential given AI's ability to evade static controls.
Table: Recommended Detection Controls Mapped to NIST CSF
| NIST CSF Function | Control | Implementation Priority |
|---|---|---|
| Identify | Asset inventory of all FortiGate instances | Immediate |
| Protect | MFA on admin access, management ACLs | Immediate |
| Detect | SIEM rules for FortiOS config changes | High |
| Detect | Behavioral anomaly detection on VPN traffic | High |
| Respond | Automated isolation playbooks | Medium |
| Recover | Tested config restore from clean backup | Medium |
Organizations subject to HIPAA, PCI DSS, or SOC 2 frameworks should treat these controls as compliance-relevant, not optional.
Threat Intelligence Integration
AI-powered attacks evolve quickly. Subscribing to Fortinet's PSIRT feed, CISA Known Exploited Vulnerabilities (KEV) catalog, and commercial threat intelligence platforms ensures your team receives timely indicators. Map new intelligence to your MITRE ATT&CK-aligned detection rules on a weekly cadence.
The Broader Shift: AI as an Adversarial Tool
What makes this trend significant is not any single attack. It is the normalization of AI as an offensive capability multiplier. Threat actors with limited technical depth can now produce sophisticated, evasion-aware exploits by leveraging public AI models.
Defenders must respond in kind — using AI-enhanced detection, automated response playbooks, and continuous red team exercises that simulate AI-assisted attack chains. The asymmetry between offense and defense narrows when security teams embrace the same technological capabilities their adversaries are exploiting.
Key Takeaways
- Patch immediately: Unauthenticated vulnerabilities in FortiOS are the primary entry point for AI-assisted attacks — patching eliminates the most critical risk
- Monitor outbound API traffic: Unusual connections to cloud AI endpoints from network devices are a strong indicator of compromise or attacker staging
- Extend beyond signatures: AI-generated payloads evade signature detection; behavioral analytics and anomaly-based detection are now essential
- Harden management interfaces: Restricting access to FortiGate admin panels via IP allowlisting and MFA sharply reduces attack surface
- Integrate threat intel continuously: AI-assisted campaigns move faster than quarterly review cycles — automate threat feed ingestion and detection rule updates
- Exercise your response plan: Tabletop and purple team exercises that simulate AI-assisted intrusion help identify detection gaps before attackers do
Conclusion
AI-assisted attacks against FortiGate firewalls represent a meaningful escalation in the threat landscape. Adversaries are using large language models to accelerate exploit development, refine evasion techniques, and compress the time to initial access in enterprise environments. The vulnerability surface is real, the techniques are documented, and the impact spans critical infrastructure globally.
The path forward is clear. Prioritize patching for high-severity FortiOS CVEs, harden management access, and invest in behavioral detection capabilities that can identify what signature-based tools miss. Security teams that treat AI as both an offensive risk and a defensive opportunity will be best positioned to stay ahead of this evolving threat.
Start with your exposure assessment today. Know which FortiGate devices face the internet, confirm their patch status, and build your detection logic around the IOCs outlined in this article.
Frequently Asked Questions
Q: Do attackers need advanced skills to use AI for FortiGate exploitation? A: No — that is precisely what makes this trend alarming. AI models lower the technical barrier significantly, enabling moderately skilled threat actors to generate functional exploits and evasion techniques without deep programming expertise.
Q: Which FortiOS versions are most at risk from these AI-assisted attacks? A: Versions affected by CVE-2022-42475, CVE-2023-27997, and CVE-2024-21762 carry the highest risk due to their unauthenticated attack vectors. Fortinet's PSIRT advisories provide the definitive list of affected version branches and corresponding patches.
Q: How can I tell if AI-generated tools were used in an attack against my environment? A: Direct attribution to AI is difficult, but behavioral signals help. Look for unusual outbound API calls, rapidly mutating credential attempts, syntactically varied exploit traffic, and configuration changes that lack a corresponding change ticket.
Q: Does disabling SSL-VPN fully protect a FortiGate device? A: Disabling SSL-VPN removes one significant attack surface, but it does not fully harden a device. Management interface exposure, firmware vulnerabilities in other service components, and weak credential policies all remain as potential entry points.
Q: How should organizations update their threat intelligence programs to address AI-powered attacks? A: Shift from periodic review cycles to continuous, automated ingestion of threat feeds including Fortinet PSIRT, CISA KEV, and MITRE ATT&CK updates. Map new intelligence directly to detection rules and run monthly exercises simulating AI-assisted attack scenarios.