Semantic Probe-Based Vulnerability Testing
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Solution Overview
Problem
Current security testing tools for software applications in networked environments are inefficient due to high costs associated with testing all possible payloads, leading to limited vulnerability testing and failure to effectively bypass defense mechanisms like sanitization and validation processes.
Innovation Solution
The method involves generating probes to determine application vulnerabilities, focusing on high-exposure areas with a small number of test trials, using a black-box scheme with heuristics to detect defense strategies and converge on a bypass, and employing a graph-search problem to efficiently test vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible payloads are tested to ensure comprehensive vulnerability detection, then measurement precision is improved, but loss of energy and loss of time increase significantly
Solution Approach 1:
The patent segments the vulnerability testing process into two distinct phases: probing phase and testing phase. The probing phase uses a small set of probes to gather semantic information about the application's defense mechanisms, while the testing phase uses this information to generate targeted payloads. This segmentation allows comprehensive vulnerability detection without requiring exhaustive testing of all possible payloads, thereby reducing energy loss while maintaining measurement precision.
Solution Approach 2:
The patent performs preliminary probing actions before the main testing phase. By sending a small number of probes to detect sanitization and validation processes in advance, the system gathers information that guides subsequent payload generation. This preliminary action enables the testing phase to focus only on relevant vulnerability vectors, avoiding wasted energy on ineffective payloads while ensuring comprehensive coverage of actual weaknesses.
2Loss of energy
If a small number of payloads are tested to reduce testing costs, then loss of energy is reduced, but measurement precision deteriorates due to limited vulnerability coverage
Solution Approach 1:
The patent implements a feedback mechanism where the results of probing phases inform the payload generation in the testing phase. The probes detect the application's sanitization and validation processes, and this information feeds back into the payload generation algorithm. This feedback loop ensures that the limited number of payloads tested are highly targeted and relevant, maintaining measurement precision while minimizing energy consumption by avoiding futile testing attempts.
Solution Approach 2:
The patent changes the parameters of the testing approach by shifting from a static, exhaustive payload list to a dynamic, adaptive payload generation process. The testing tool modifies payload characteristics based on real-time feedback from probes about the application's defense mechanisms. This parameter change enables the system to achieve comprehensive vulnerability detection with fewer payloads, resolving the contradiction between testing cost and detection accuracy.
3Reliability
If exhaustive testing of all payload combinations is performed to ensure complete vulnerability coverage, then reliability is improved, but productivity decreases due to the large number of test trials required
Solution Approach 1:
The patent segments the testing process into information-gathering probes and targeted tests. This segmentation eliminates the need for exhaustive payload combinations by first understanding the application's security posture through probes, then applying only the relevant attack vectors. The result is complete vulnerability coverage achieved with far fewer total test trials, thus maintaining reliability while dramatically improving productivity.
Solution Approach 2:
The patent applies partial action by performing only the necessary probing and testing required to detect vulnerabilities, rather than exhaustively testing all possible payloads. The probing phase gathers sufficient information to guide targeted testing, and the testing phase applies only the relevant payloads needed to confirm vulnerabilities. This partial action approach achieves complete vulnerability detection without the excessive productivity cost of testing all possible payload combinations.
4Measurement precision
If the testing tool attempts many payloads per HTTP parameter to ensure thorough testing, then measurement precision is improved, but loss of time increases due to the large number of requests
Solution Approach 1:
The patent performs preliminary probing to detect the application's sanitization and validation processes before launching the full testing campaign. This preliminary action provides critical information about which payload types are likely to succeed, allowing the testing phase to skip ineffective payloads and focus on promising attack vectors. The result is accurate vulnerability detection achieved in less time by avoiding futile testing attempts.
Solution Approach 2:
The patent uses feedback from the probing phase to dynamically adjust the testing strategy. The probes provide information about the application's security mechanisms, and this feedback guides the selection and generation of payloads in the testing phase. This feedback-driven approach ensures that time is spent only on relevant vulnerability testing, maintaining measurement precision while significantly reducing overall testing duration.
Data Source
AI summary
Optimized testing of vulnerabilities in an application implemented by a method includes generating a first probe directed to determine whether an application is vulnerable to a first type of attack; analyzing one or more responses from the application based on the application responding to the first probe; in response to determining that the one or more responses from the application validate a first hypothesis about one or more vulnerabilities associated with the application, and generating at least a second probe to further verify the first hypothesis. The second probe focuses on discovering additional details about the application's vulnerabilities to the first type of attack or a second type of attack.