Penetration Testing

Automated exploitation of known vulnerabilities is more a matter of pattern matching and tool orchestration, while discovering new vulnerabilities from scratch is the core challenge of security research.

AI can greatly accelerate the vulnerability discovery process, but vulnerability patching still relies on manual processes and organizational coordination.

A standard penetration testing process is typically divided into five phases: reconnaissance, scanning, exploitation, post-exploitation, and reporting.

Core application areas of AI in penetration testing include rapidly organizing target assets during the information gathering phase, assisting with understanding vulnerability principles during the vulnerability analysis phase, and vulnerability validation and report generation.

Any penetration test must have a written authorization agreement that clearly specifies the testing scope, time window, and permitted technical means

Unauthorized penetration testing is illegal in any jurisdiction.

Traditional description scanners only read tool definitions, and web/API penetration testing only checks endpoints; neither simulates the way an attacker would actually operate along the Agent's decision path.

Skill encapsulation refers to packaging a complete penetration testing process (such as an unauthorized access detection process) into a reusable Skill to improve testing efficiency.

Penetration testing refers to security personnel simulating the techniques of real attackers to probe and attempt exploitation of a target system within an authorized scope, in order to discover vulnerabilities that can actually be exploited; it typically includes stages such as information gathering, vulnerability scanning, manual exploitation, privilege escalation, and report writing.

This Codex skill cannot replace professional penetration testing, but it can serve as a first low-cost screening; its output should be treated as leads rather than conclusions, and suspicious items still need manual review after being found.

For AI hardware, penetration testing needs to cover the entire "device—cloud—App" chain: on the device side, whether physical interfaces and debug ports are exposed; on the cloud side, API privilege escalation and data storage encryption; on the App side, whether communication certificate pinning can be bypassed.

Experts suggest that manufacturers should introduce security assessments at the product design stage and conduct penetration testing regularly.

The core difference between SRC bug hunting and penetration testing is: SRC usually targets real production environments, testing must be within the authorized scope, and it emphasizes the real harm and reproducibility of vulnerabilities.

CTF challenges are usually designed to be self-contained, clearly bounded, and have a single expected solution, mostly corresponding to some known vulnerability or technique, whereas real penetration testing faces unknown architectures, custom code, and dynamically changing defenses.

AI lowers the barrier to penetration testing, and also lowers the barrier to abuse.

Vulnerability scanning is much cheaper than penetration testing; penetration testing costs more because skilled testers must invest substantial manual time.

Mature products typically use continuous scanning and manual penetration testing in parallel, at least once a year and after major releases.

Avoid conducting penetration testing before fixing issues found by scanning, otherwise resources are wasted, because testers may find problems the scanner could have caught.

Doing a single penetration test and then stopping is not recommended; products keep changing and testing should continue to keep up.

A retest means re-verifying after fixes are complete that the vulnerability is closed and checking whether the fixes introduced new attack surface; standard practice is to stipulate a free retest in the penetration testing contract, or to arrange a separate retest after fixes.