CN113434386B - Method, system and storage medium for fuzz testing - Google Patents
Method, system and storage medium for fuzz testing Download PDFInfo
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- CN113434386B CN113434386B CN202110580093.XA CN202110580093A CN113434386B CN 113434386 B CN113434386 B CN 113434386B CN 202110580093 A CN202110580093 A CN 202110580093A CN 113434386 B CN113434386 B CN 113434386B
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Abstract
The invention provides a fuzzy test method, a system and a storage medium, wherein the method comprises the following steps: generating translation data from the first set of data; wherein the translation data comprises a location identification; according to the state of the translation data, performing standard processing on the translation data, and selecting input data of a fuzzy test; substituting the input data into the target program to obtain a test result of the target program; and analyzing the state of the target program by applying the positioning identifier and the test result of the target program. After the input data is substituted into the target program, the test result of the target program can be directly obtained, and the result obtained after the target program can receive various input data is determined, so that the target program can be efficiently, accurately and quickly analyzed.
Description
Technical Field
The invention relates to the field of software security testing, in particular to a method, a system and a storage medium for fuzz testing.
Background
Software Testing (Software Testing) refers to the process of operating a program under specified conditions to discover program errors and evaluate whether they can meet design requirements. Each new software may have a new security defect that does not conform to all known modes at all, and it is very important to apply a good set of principles to avoid the coming-to-market of the unsafe software and to avoid the attack of the unsafe software during the software security test.
The fuzz testing is a widely used software security testing technology for discovering hidden dangers in software (such as application programs, protocol implementation bodies and the like), and the basic principle is as follows: a large number of random numbers are sent to an object to be tested (e.g., a server, a PC, etc. running relevant software), so that the object to be tested runs in an unexpected manner, thereby discovering a fault.
In the existing fuzz test, only input data of the fuzz test is recorded through a single channel, and the input data are difficult to control, so that the situation that part of the input data cannot be identified can occur, and the test efficiency is low.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a method, system and storage medium for fuzz testing are provided to efficiently perform fuzz testing.
In order to solve the technical problems, the invention adopts the technical scheme that: the application provides a fuzz testing method, which is used for carrying out fuzz testing on a target program through first set data, and comprises the following steps:
generating translation data according to the first set of data; wherein the translation data comprises a location identification;
according to the state of the translation data, performing standard processing on the translation data, and selecting input data of the fuzz test;
substituting the input data into the target program to obtain a test result of the target program;
and analyzing the state of the target program by applying the positioning identifier and the test result of the target program.
Wherein, the performing a standard process on the translation data according to the state of the translation data, and selecting the input data of the fuzz test specifically includes:
acquiring the specific field length and the encryption mode of the translation data and verifying;
if the specific field length and the encryption mode of the translation data both accord with the preset specification of the target program, the corresponding translation data is used as the input data of the fuzz test.
Further, the method further comprises:
if the specific field length of the translation data is smaller than the preset length of the target program,
according to the type of the translation data, applying corresponding preset filling characters, and filling the corresponding specific fields of the translation data;
and taking the translation data after filling processing as input data of the fuzz test.
Optionally, the method further comprises:
comparing the specific field of the translation data with a preset correction template;
according to the type of the translation data, corresponding preset change characters are applied, and the corresponding translation data are replaced;
and taking the translation data after the replacement processing as input data of the fuzz test.
Wherein the positioning identifier comprises a first positioning identifier; before generating the translation data according to the first set of data, the method further includes a step of generating the first set of data, specifically:
randomly generating at least one seed of the fuzzy test to generate original data; wherein the seed is provided with the seed label;
iterating the original data to generate iteration data;
acquiring an iteration serial number, and identifying the iteration data to generate the first set data;
the first set of data comprises a first positioning identifier and the iterative data.
The first positioning identifier comprises a seed number and an iteration serial number.
Further, the positioning identifier includes a second positioning identifier, and the performing the specification processing on the translation data and selecting the input data of the fuzz test further includes:
and resolving the translation data, and taking the content logic of the translation data as the second positioning identifier.
The method is based on go language and is used for testing scripts in multiple languages.
A second aspect of the present application provides a fuzz testing system for fuzz testing a target program through a first set of data, the system comprising:
the translation module is used for generating translation data according to the first set data; wherein the translation data comprises a location identification;
the normalization module is used for performing normalization processing on the translation data according to the state of the translation data and selecting input data of the fuzz test;
the test module is used for substituting the input data into the target program to obtain a test result of the target program;
and the backtracking module is used for applying the positioning identifier and the test result of the target program and analyzing the state of the target program.
A third aspect of the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method as described above.
The invention has the beneficial effects that: the method has the advantages that the positioning identifier is arranged in the translation data so as to trace back specific data in the first set of data, so that later maintenance processing is facilitated, and the probability of generation of invalid data in the future is reduced. And according to the state of the translation data, performing specification processing, and removing the translation data which completely does not conform to the target program, and leaving the specification data to avoid operation faults in the fuzz test process. And after the input data is substituted into the target program, the test result of the target program can be directly obtained, and the result obtained after the target program can receive various input data is determined, so that the target program is efficiently, accurately and quickly analyzed.
Drawings
The detailed structure of the invention is described in detail below with reference to the accompanying drawings
FIG. 1 is an overall flow chart of the fuzz testing of the first embodiment of the present invention;
FIG. 2 is a flow chart of selecting input data for fuzz testing according to a second embodiment of the present invention;
FIG. 3 is a flow chart of selecting input data for fuzz testing according to a third embodiment of the present invention;
FIG. 4 is a flowchart of a fourth embodiment of the present invention for selecting input data for fuzz testing;
FIG. 5 is a flow chart of generating a first set of data according to a fifth embodiment of the present invention;
FIG. 6 is a block diagram of a fuzz testing system according to a sixth embodiment of the present invention.
Detailed Description
In order to explain technical contents, structural features, and objects and effects of the present invention in detail, the following detailed description is given with reference to the accompanying drawings in conjunction with the embodiments.
Referring to fig. 1, a first aspect of the present application provides a fuzz testing method for performing a fuzz test on a target program through a first set of data, the method including the following steps:
step S101, generating translation data according to the first set data; wherein the translation data comprises a location identifier;
step S102, according to the state of the translation data, performing standard processing on the translation data, and selecting input data of a fuzzy test;
step S103, substituting the input data into the target program to obtain a test result of the target program;
and step S104, applying the positioning identification and the test result of the target program, and analyzing the state of the target program.
The method has the advantages that the positioning identifier is arranged in the translation data so as to trace back specific data in the first set of data, so that later maintenance processing is facilitated, and the probability of generation of invalid data in the future is reduced. According to the state of the translation data, the translation data which is completely out of compliance with the target program is cleared, and the standard data is left, so that the operation fault in the fuzz test process is avoided. After the input data is substituted into the target program, the test result of the target program can be directly obtained, and the result obtained after the target program can receive various input data is determined, so that the target program can be efficiently, accurately and quickly analyzed.
It should be understood that the target program in this embodiment, generally referred to as a third-party script program, may be in different languages from the original data.
It needs to be understood that the technical scheme of the application realizes the effect of loose coupling, and any interface required by the fuzz test is replaceable; in addition, the first set data can be called through popular languages such as C, JAVA, PHP, go, python and the like, and can be compatible with functions of various languages so as to perform mutation processing on specific input data.
More importantly, after the technical scheme of the application is combined with go language and a problem occurs in the fuzzy test, the processing can be continued, and after the fuzzy test is carried out in a paragraph, the original data of the fuzzy test can be traced through the corresponding token. In addition, after the technical scheme of the application is combined with a specific package issuing technology, the fuzzy test can be simultaneously carried out on a plurality of objects, and the debugging speed is high.
Referring to fig. 2, according to the state of the translation data, the translation data is subjected to a specification process to select input data of the fuzz test, which specifically includes:
s200, acquiring the specific field length and the encryption mode of the translation data and verifying;
in step S201, if the specific field length and the encryption manner of the translation data both conform to the preset specification of the target program, the corresponding translation data is used as the input data of the fuzz test.
It should be appreciated that there is uncertainty in generating translation data from the first set of data, thereby resulting in some information in the translation data that is not recognizable by the target program. In the process of using the conventional technical solution to perform the translation, only the verification is generally performed on the data content. However, the content of the data varies widely, and mistakes and omissions are easily encountered. Therefore, after long-term research and experiments, specific information of the translation data can be identified by using the specific field length and the encryption mode of the translation data compared with the traditional data identification, and corresponding input data can be selected accordingly. Therefore, the method has the advantages of high efficiency and accuracy, and the running state of the target program can be better tested.
Referring to fig. 3, performing a normalization process on the translation data according to the state of the translation data to select input data for the fuzz test further includes:
step S300, if the specific field length of the translation data is smaller than the preset length of the target program;
step S301, according to the type of the translation data, applying a corresponding preset filling character to fill a specific field of the corresponding translation data;
step S302, the translation data after the filling processing is used as input data of the fuzz test.
Generally, during the data translation, a situation of losing data may occur; in the technical scheme, from the perspective of translating the data state, the corresponding preset padding characters can be directly applied for padding.
It should be understood that the translation data having specific fields and bugs appearing during the translation process is generally important data required for the fuzzy test, and these data are retained to trace back to the corresponding original data, so that the problem of the target program can be detected more easily.
Referring to fig. 4, performing a normalization process on the translation data according to the state of the translation data to select input data for the fuzz test further includes:
step S401, comparing a specific field of the translation data with a preset correction template;
step S402, according to the type of the translation data, applying the corresponding preset change character to replace the corresponding translation data;
in step S403, the translation data after the replacement processing is used as input data for the fuzz test.
In the embodiment, the correction is mainly performed on the specific field of the translation data, and since the bug may occur on only a few characters after the error occurs on the specific field of the data, the bug may occur on only a few characters.
It should be further understood that the translation data having specific fields and bugs occurring during the translation process is generally important data required for the fuzzy test, and these data are retained to trace back to the corresponding original data, so that the problem of the target program can be detected more easily. In the technical scheme, the translation data is corrected by formulating a preset change program so as to more accurately acquire the problems of the target program.
Referring to fig. 5, the positioning mark includes a first positioning mark; before generating the translation data according to the first set of data, the method further includes a step of generating the first set of data, specifically:
s501, randomly generating at least one seed of the fuzzy test to generate original data; wherein, the seeds are provided with seed marks;
step S502, iteration is carried out on the original data to generate iterative data;
step S503, obtaining an iteration serial number, identifying iteration data, and generating first set data;
the first set of data includes a first positioning identifier and iteration data.
In the scheme, an explicit token is formulated, so that a traceable backtracking track is formed. By applying the technical scheme of the embodiment, data at any position can be selected and collected so as to more clearly acquire problems in the target program.
Specifically, the first positioning identifier includes a seed number and an iteration sequence number.
It should be understood that the seed number represents the seed for performing the fuzz test, and the iteration sequence number is a sequence number generated based on the seed number. After the fuzzing test is performed and the fuzzing test result is obtained, the seed number may be determined first, and then the iteration sequence number may be determined, so as to obtain the specific data corresponding to the fuzzing test result in the first set of data. And the calculation force required by the process is less, the calculation speed is higher, and the calculation speed can be obtained more quickly.
Specifically, the positioning identifier includes a second positioning identifier, performs specification processing on the translation data, and selects input data of the fuzz test, and further includes:
and analyzing the translation data, and taking the content logic of the translation data as a second positioning identifier.
Therefore, by using the implicit token, when the first positioning identifier is lost or does not exist, the normal operation of the fuzz test can be maintained.
It will be appreciated that any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and that the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Referring to fig. 6, a second aspect of the present application provides a fuzz testing system for fuzz testing a target program through a first set of data, comprising:
the translation module is used for generating translation data according to the first set data; wherein the translation data comprises a location identification;
the standard module is used for performing standard processing on the translation data according to the state of the translation data and selecting input data of the fuzzy test;
the test module is used for substituting the input data into the target program to obtain a test result of the target program;
and the backtracking module is used for analyzing the state of the target program by applying the positioning identifier and the test result of the target program.
Further, the system further comprises:
the verification module is used for acquiring the specific field length and the encryption mode of the translation data and verifying the specific field length and the encryption mode;
the verification module is further used for taking the corresponding translation data as input data of the fuzz test if the specific field length and the encryption mode of the translation data both accord with the preset specification of the target program.
The modules are essentially virtual modules, and carry the methods in the embodiments. The modules can be combined by any practical product.
A terminal comprising a processor, a memory, and a display, the processor coupled to the memory and the display, the memory having stored thereon a computer program executable on the processor; the processor executes the computer program to realize the method.
The present application also relates to a storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method described above.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are exemplary and should not be construed as limiting the present application and that changes, modifications, substitutions and alterations in the above embodiments may be made by those of ordinary skill in the art within the scope of the present application.
Through the above description of the embodiments, it is obvious for those skilled in the art that the above embodiment method can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g., a Read Only Memory (ROM)/Random Access Memory (RAM), a magnetic disk, an optical disk), and includes several instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like. Persons of ordinary skill in the art will appreciate that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware associated with instructions of a program, which may be stored in a computer-readable storage medium, and the program, when executed, includes one or a combination of the steps of the method embodiments
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The above description is only an embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields are included in the scope of the present invention.
Claims (8)
1. A fuzz testing method for fuzz testing of a target program through a first set of data, the method comprising the steps of:
randomly generating at least one seed of the fuzzy test to generate original data; wherein, the seeds are provided with seed labels; iterating the original data to generate iteration data; acquiring an iteration serial number, and identifying the iteration data to generate the first set data; wherein the first set of data comprises a first positioning identifier and the iterative data; the first positioning identifier is an explicit token, and the first positioning identifier includes: the seed label and the iteration sequence number;
generating translation data according to the first set of data; wherein the translation data comprises a location identification;
analyzing the translation data according to the state of the translation data, taking the content logic of the translation data as a second positioning identifier, and selecting the input data of the fuzz test; wherein the second positioning identifier is an implicit token;
substituting the input data into the target program to obtain a test result of the target program;
and analyzing the state of the target program by applying the positioning identifier and the test result of the target program.
2. The fuzz testing method of claim 1, wherein the performing specification processing on the translation data according to the state of the translation data and selecting the input data for the fuzz testing comprises:
acquiring the specific field length and the encryption mode of the translation data and verifying;
and if the specific field length and the encryption mode of the translation data both accord with the preset specification of the target program, taking the corresponding translation data as the input data of the fuzz test.
3. The fuzz testing method of claim 2, wherein the method further comprises:
if the specific field length of the translation data is smaller than the preset length of the target program,
according to the type of the translation data, applying corresponding preset filling characters, and filling the corresponding specific fields of the translation data;
and taking the translation data after filling processing as input data of the fuzz test.
4. The fuzz testing method of claim 2, wherein the method further comprises:
comparing the specific field of the translation data with a preset correction template;
according to the type of the translation data, corresponding preset change characters are applied, and the corresponding translation data are replaced;
and taking the translation data after the replacement processing as input data of the fuzz test.
5. The fuzz testing method of claim 1, wherein: the first positioning identification comprises a seed number and an iteration serial number.
6. The fuzz testing method of claim 1, wherein: the method is based on a go language and is used for testing multiple language scripts.
7. A fuzz testing system for fuzz testing of a target program with a first set of data, the system comprising:
the generating module is used for randomly generating at least one seed of the fuzzy test to generate original data; wherein, the seeds are provided with seed labels; iterating the original data to generate iteration data; acquiring an iteration serial number, and identifying the iteration data to generate the first set data; wherein, the first set data comprises a first positioning identifier and the iterative data; the first positioning identifier is an explicit token, and the first positioning identifier includes: the seed label and the iteration sequence number;
the translation module is used for generating translation data according to the first set data; wherein the translation data comprises a location identification;
the specification module is used for analyzing the translation data according to the state of the translation data, taking the content logic of the translation data as a second positioning identifier and selecting the input data of the fuzz test; wherein the second positioning identifier is an implicit token;
the test module is used for substituting the input data into the target program to obtain a test result of the target program;
and the backtracking module is used for applying the positioning identifier and the test result of the target program and analyzing the state of the target program.
8. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, realizing the steps of the method of any one of claims 1 to 6.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109739755A (en) * | 2018-12-27 | 2019-05-10 | 北京理工大学 | A kind of fuzz testing system executed based on program trace and mixing |
| CN112069061A (en) * | 2020-08-19 | 2020-12-11 | 北京科技大学 | Software security vulnerability detection method and system for deep learning gradient guidance variation |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8762948B1 (en) * | 2012-12-20 | 2014-06-24 | Kaspersky Lab Zao | System and method for establishing rules for filtering insignificant events for analysis of software program |
| US10452526B2 (en) * | 2017-06-15 | 2019-10-22 | Microsoft Technology Licensing, Llc | Machine learning for constrained mutation-based fuzz testing |
| US11151018B2 (en) * | 2018-04-13 | 2021-10-19 | Baidu Usa Llc | Method and apparatus for testing a code file |
| CN109634870A (en) * | 2018-12-20 | 2019-04-16 | 国家计算机网络与信息安全管理中心 | A kind of script management method of industrial control system agreement fuzz testing |
| CN109885479B (en) * | 2019-01-07 | 2022-02-01 | 中国人民解放军战略支援部队信息工程大学 | Software fuzzy test method and device based on path record truncation |
| CN111913876B (en) * | 2020-07-03 | 2023-06-27 | 北京惠而特科技有限公司 | AFL fuzzy test method and device for industrial control DPI engine and electronic equipment |
-
2021
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109739755A (en) * | 2018-12-27 | 2019-05-10 | 北京理工大学 | A kind of fuzz testing system executed based on program trace and mixing |
| CN112069061A (en) * | 2020-08-19 | 2020-12-11 | 北京科技大学 | Software security vulnerability detection method and system for deep learning gradient guidance variation |
Non-Patent Citations (3)
| Title |
|---|
| Learning to Fuzz from Symbolic Execution with Application to Smart Contracts;Jingxuan He 等;《the 2019 ACM SIGSAC Conference》;20191106;第531-548页 * |
| 基于定向模糊测试技术的缺陷检测系统研究与实现;张旖旎;《中国优秀硕士学位论文全文数据库 信息科技辑》;20190815;I138-250 * |
| 基于突变的模糊测试;大1234草;《https://blog.csdn.net/sinat_38816924/article/details/113445277》;20210130;第1-4页 * |
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