Vulnerability Discovery

In the SRC vulnerability mining field, there is a widely used 'universal three-step method for finding vulnerabilities': information gathering, packet capture analysis with parameter tampering to verify vulnerabilities, and validating vulnerabilities and writing reports.

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.

The core application points of AI in penetration testing are rapidly enumerating target assets during the information gathering phase, assisting in understanding principles during the vulnerability analysis phase, and vulnerability validation and report generation.

According to the original video, a single person might submit dozens to over a hundred vulnerabilities per year using traditional methods, whereas with AI-assisted vulnerability hunting, some individuals have submitted more than 14,000 vulnerabilities in a year.

In fields requiring precision such as mathematical proof and vulnerability discovery, massive attempts may bring a lot of noise, and truly valuable signals need strict verification mechanisms to extract.

Mathematical proof search and vulnerability discovery are similar in computational form: both search for specific patterns in a huge search space and both require extensive trial and error and verification.

The universal three-step method for SRC vulnerability hunting is understanding the target, discovering vulnerabilities, and writing a report.

In security scenarios, Skills can solidify detection approaches, payload construction methods, and report templates for common vulnerabilities into reusable modules, enabling agents to invoke a unified standard process.

The workflow (orchestration quality) determines the final output of AI-assisted vulnerability discovery, and merely connecting a model does not automatically find vulnerabilities.

The underlying methodology of AI-assisted vulnerability discovery (information gathering, vulnerability verification, results output) is the same as manual vulnerability hunting; AI does not change the methodology but can lower the execution cost of each step.

The core value of AI Agent vulnerability hunting can be summarized in three points: lowering the learning threshold, greatly improving efficiency, and letting people focus on high-value stages (vulnerability verification and report quality control).

The core abilities of vulnerability hunting—deep understanding of the target system and sensitivity to anomalous behavior—are very hard to gain by watching videos, and must come through repeated practice against large numbers of real targets.

Newly launched features often lack sufficient adversarial testing, and security reviews may only cover the main flow, leaving edge cases, abnormal inputs, and permission intersection zones easily blank, so the duplicate rate is lower and the probability of being first is higher.

Choice affects output more than effort: when resources are limited, the judgment to identify high-value targets is often more critical than mastering more tools.

Traditional SRC vulnerability hunting can be summarized as a three-step method: information gathering, packet capture analysis and testing, and verification and report writing.

In traditional vulnerability hunting, breaking through knowledge barriers typically requires three to six months of learning

In the AI-assisted vulnerability hunting workflow, the human role shifts to decision-making and verification—determining whether a vulnerability is genuine and writing the report for submission.

Conducting vulnerability probing on systems outside the scope of authorization is illegal in most jurisdictions, and users bear full responsibility for ensuring they have lawful authorization for the targets they test.

It often takes three to six months for beginners to reach an effective vulnerability-hunting state and solidify their fundamentals.

When no vulnerabilities are found after a long time, one should look for new variants within familiar vulnerability types rather than stubbornly fixating on a single vulnerability type, while also consolidating foundational skills.