How IT Professionals Risk Leaking Confidential Data When Using ChatGPT and Other LLMs

Artificial intelligence tools such as ChatGPT, Claude and Gemini have become daily instruments for network engineers, SOC analysts and system administrators who use them to analyze logs, debug configurations and generate scripts. The convenience comes with a serious risk: employees frequently paste large volumes of internal data into these cloud services without considering what information leaves the organization. Real-world examples include SOC teams uploading multi-thousand-line logs containing internal IP addresses, employee emails and authentication tokens, as well as network engineers sending running-config files from Cisco, FortiGate and Palo Alto devices. These files reveal VLAN structures, VPN peers, SNMP community strings and LDAP server addresses, providing attackers with valuable reconnaissance material. The Malwarebytes research team documented concrete cases where the Share function in AI platforms exposed sensitive corporate information. The underlying driver is not negligence but the universal desire to complete routine tasks faster, turning an efficiency tool into a potential data-exfiltration vector for banks, government agencies and healthcare organizations.

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Artificial intelligence has fundamentally changed how IT professionals work. Network engineers, security specialists, system administrators, DevOps engineers and SOC analysts now routinely rely on ChatGPT, Claude and Gemini to parse configuration files, locate errors, explain unexpected behavior and draft scripts.

The time savings are substantial, yet a critical question is often overlooked: exactly what data is being sent to these cloud-based services?

The human factor behind data exposure

The issue is not limited to junior staff. Both experienced engineers and newcomers face the same pressure to resolve problems quickly. In almost every security incident, the root cause is the desire to bypass repetitive manual work rather than any sophisticated attacker technique.

When an engineer pastes a multi-thousand-line log into an LLM with the request to “find anomalies and successful logins,” internal IP addresses, server names, domain information, employee email addresses and authentication tokens travel to the provider’s infrastructure.

Network configuration files present even greater risk

The same pattern occurs with device configurations. Engineers commonly upload running-config files from Cisco, FortiGate and Palo Alto devices to accelerate troubleshooting of ACLs or routing issues. Although passwords may be hashed, the files still disclose:

  • Internal and external IP addressing schemes
  • VLAN and DMZ topology
  • VPN peer addresses and encryption parameters
  • Hostnames, interface descriptions and branch names
  • SNMP community strings and LDAP server locations

For government agencies, banks and healthcare providers, this information constitutes a direct reconnaissance vector.

Storage and access guarantees remain uncertain

Many users treat conversations with large language models as private notes. In reality, no provider offers absolute assurances regarding long-term storage, internal access by employees or subsequent use of the data. On 28 July 2026 the Malwarebytes research team published an analysis of real incidents triggered by the platform’s “Share” feature, confirming that sensitive corporate data had left organizational boundaries.

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