Fake ITSM Tickets Allow Attackers to Trick AI SOC Agents into Dismissing Real Incidents
An experiment conducted with the DeepSeek model demonstrates that AI SOC agents can be systematically deceived without any prompt injection. Instead of embedding hidden commands, an attacker simply plants a plausible change request in the organization’s ITSM system.
The scenario begins with an attacker who possesses credentials sufficient to edit existing tickets but cannot alter audit logs or role assignments. The adversary locates an already-approved ticket, updates the target server and maintenance window to match an ongoing attack, and leaves the approval stamp untouched. When the SIEM raises an alert for backup deletion or other suspicious activity, the agent retrieves the ticket, observes that the actions align with the documented work, and closes the alert.
The same link later reassures a human analyst reviewing overnight closures. Because the ticket appears valid on its face, the analyst may accept the agent’s conclusion without deeper verification.
Researchers tested the approach on 96 cases—48 simulated attacks and 48 legitimate maintenance activities—each run three times. When a clean forged ticket was present, the agent closed every attack in all 72 runs. Classic prompt-injection filters offered no protection because no instructions were hidden in the ticket text.
Three LLM-centric defenses were evaluated. Strict system prompts reduced erroneous closures of attacks but also prevented the agent from closing most genuine work. A secondary “critic” model produced similar results. Supplying audit logs and role directories improved detection only when combined with rigid rules; otherwise the model still accepted roughly a quarter of the forged tickets.
The most effective control removed the final decision from the language model entirely. A small deterministic function examines three immutable sources: the ticket-system journal, the employee and role directory, and the time/server constraints listed in the ticket. If any check fails, the alert is escalated rather than closed. In the experiment this gate blocked every forged ticket while still automating 97 percent of legitimate changes.
The authors note that the same principle appears in Google DeepMind CaMeL research: when an AI agent must act on potentially untrusted data, critical authorization decisions should be enforced by non-LLM code that cannot be persuaded.
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