Big Data Applications in Healthcare Research

AI agents are increasingly trusted to scan files for malware and flag security threats on their own, work once handled by human analysts and traditional security tools. This opens a new kind of attack. Rather than hiding from antivirus engines or sandboxes, an attacker can target the judgment of the AI itself, planting content inside a file that is designed to mislead the agent examining it. We present a proof-of-concept showing that a simple "flag", the kind of reward token used in capture-the-flag (CTF) security competitions, placed next to malicious code can reliably hijack an AI scanning agent, causing it to report the flag and end its session instead of warning about the malware it was supposed to find.


We deployed a safely disabled piece of malware that hides two genuine malicious pieces of code; The former secretly contacts an attacker-controlled server and the latter configures itself to relaunch whenever the computer restarts. A capable scanning agent should uncover and report both of them. Hidden alongside them are three lures aimed squarely at the AI model. The first impersonates a trusted scanner, announces that the file has already been cleared, and tells the agent to stop. The other two exploit a habit picked up by agents trained on CTF challenges: one plants a tempting "flag" that lures the agent into declaring victory and quitting, and the other adds a reassuring "safe" label meant to trick the agent into calling the file harmless.We find that affected agents frequently seize on the planted flag, declare the task complete, and shut down, sometimes even after they have begun to uncover the real threats. In effect, the flag overrides the agent's actual mission: the file under inspection successfully issues a stop command to the very system inspecting it. Because each lure is harmless and clearly marked, we can tell manipulation apart from genuine analysis. This reveals a gap that accuracy-focused evaluation misses: being good at finding malware and being hard to mislead are two different abilities, and an agent strong at one can fail completely at the other.


The proof-of-concept highlights a concrete and emerging attack, deceiving AI agents through planted instructions and fake rewards, and a clear defensive lesson: a scanning agent must treat the content it analyzes as untrusted data, never as instructions to obey. The same trick generalizes across file types and behaviors, suggesting that as AI code scanners become widespread, attackers will increasingly hide such lures directly inside their payloads. We argue this novel form of attack deserves serious attention in both offensive and defensive agent design.