Agentic pentesting promises smarter orchestration of tools, but it does not magically eliminate false positives. At its core, an agent still leans on the same scanners, payload generators, and detection heuristics that produced noisy results in the first place. If the underlying tools misclassify behavior or lack application context, the agent simply becomes a faster, more automated way to generate and route those misclassifications. In other words, you risk “scaling the noise” as much as scaling the signal.
Another limitation is that most agentic systems still struggle with business context and intent, which is where many false positives are born. A finding that looks critical in HTTP traces might be benign in the real-world workflow because of compensating controls, domain‑specific logic, or risk acceptance decisions that only humans understand. Agents can replay exploits and correlate signals, but they cannot reliably answer questions like “Is this test user data or real PII?” or “Would exploiting this actually harm the business?” Without that judgment, they often cannot confidently close the loop on whether something is truly a vulnerability or just an academic issue.
Finally, agentic pentesting introduces its own new sources of error that can masquerade as false positives. Misconfigured prompts, overly broad goals, or aggressive automation can lead agents to test unsupported flows, mis-handle authentication, or misinterpret application responses. These mistakes can create “findings” that look real on paper but collapse under minimal human scrutiny. So while agentic approaches can help prioritize, group, and sometimes auto‑retest issues, they do not remove the need for human validation; they merely change where you spend your validation effort—from sifting through raw scanner output to scrutinizing AI‑curated results.
