Black Box Security Analysis and Test Techniques

Black box techniques are the only techniques available for analyzing and testing nondevelopmental binary executable without first decompiling or disassembling them. Black box tests are not limited in utility to COTS and other executable packages: they are equally valuable for testing compiled custom developed and open source code, enabling the tester to observe the software’s actual behaviors during execution and compare them with behaviors that could only be speculated upon based on extrapolation from indicators in the source code. Black box testing also allows for examination of the software’s interactions with external entities (environment, users, attackers)—a type of examination that is impossible in white box analyses and tests. One exception is the detection of malicious code. On the other hand, because black box testing can only observe the software as it runs and “from the outside in,” it also provides an incomplete picture.

For this reason, both white and black box testing should be used together, the former during the coding and unit testing phase to eliminate as many problems as possible from the source code before it is compiled, and the latter later in the integration and assembly and system testing phases to detect the types of byzantine faults and complex vulnerabilities that only emerge as a result of runtime interactions of components with external entities. Specific types of black box tests include:

Binary Security Analysis

This technique examines the binary machine code of an application for vulnerabilities. Binary security analysis tools usually occur in one of two forms. In the first form, the analysis tool monitors the binary as it executes, and may inject malicious input to simulate attack patterns intended to subvert or sabotage the binary’s execution, in order to determine from the software’s response whether the attack pattern was successful. This form of binary analysis is commonly used by web application vulnerability scanners. The second form of binary analysis tool models the binary executable (or some aspect of it) and then scans the model for potential vulnerabilities. For example, the tool may model the data flow of an application to determine whether it validates input before processing it and returning a result. This second form of binary analysis tool is most often used in Java bytecode scanners to generate a structured format of the Java program that is often easier to analyze than the original Java source code.

Software Penetration Testing

Applies a testing technique long used in network security testing to the software components of the system or to the software-intensive system as a whole. Just as network penetration testing requires testers to have extensive network security expertise, software penetration testing requires testers who are experts in the security of software and applications. The focus is on determining whether intra-or inter-component vulnerabilities are exposed to external access, and whether they can be exploited to compromise the software, its data, or its environment and resources. Penetration testing can be more extensive in its coverage and also test for more complex problems, than other, less sophisticated (and less costly) black box security tests, such as fault injection, fuzzing, and vulnerability scanning. The penetration tester acts, in essence, as an “ethical hacker.” As with network penetration testing, the effectiveness of software penetration tests is necessarily constrained by the amount of time, resources, stamina, and imagination available to the testers.

Fault Injection of Binary Executable

This technique was originally developed by the software safety community to reveal safety-threatening faults undetectable through traditional testing techniques. Safety fault injection induces stresses in the software, creates interoperability problems among components, and simulates faults in the execution environment. Security fault injection uses a similar approach to simulate the types of faults and anomalies that would result from attack patterns or execution of malicious logic, and from unintentional faults that make the software vulnerable. Fault injection as an adjunct to penetration testing enables the tester to focus in more detail on the software’s specific behaviors in response to attack patterns. Runtime fault injection involves data perturbation. The tester modifies the data passed by the execution environment to the software, or by one software component to another. Environment faults in particular have proven useful to simulate because they are the most likely to reflect real-world attack scenarios. However, injected faults should not be limited to those that simulate real-world attacks. To get the most complete understanding of all of the software’s possible behaviors and states, the tester should also inject faults that simulate highly unlikely, even “impossible,” conditions. As noted earlier, because of the complexity of the fault injection testing process, it tends to be used only for software that requires very high confidence or assurance.

Fuzz Testing

Like fault injection, fuzz testing involves the input of invalid data via the software’s environment or an external process. In the case of fuzz testing, however, the input data is random (to the extent that computer-generated data can be truly random): it is generated by tools called fuzzers, which usually work by copying and corrupting valid input data. Many fuzzers are written to be used on specific programs or applications and are not easily adaptable. Their specificity to a single target, however, enables them to help reveal security vulnerabilities that more generic tools cannot.

Byte Code, Assembler Code, and Binary Code Scanning

This is comparable to source code scanning but targets the software’s uninterpreted byte code, assembler code, or compiled binary executable before it is installed and executed. There are no security-specific byte code or binary code scanners. However, a handful of such tools do include searches for certain security-relevant errors and defects; see for a fairly comprehensive listing.

Automated Vulnerability Scanning

Automated vulnerability scanning of operating system and application level software involves use of commercial or open source scanning tools that observe executing software systems for behaviors associated with attack patterns that target specific known vulnerabilities. Like virus scanners, vulnerability scanners rely on a repository of “signatures,” in this case indicating recognizable vulnerabilities. Like automated code review tools, although many vulnerability scanners attempt to provide some mechanism for aggregating vulnerabilities, they are still unable to detect complex vulnerabilities or vulnerabilities exposed only as a result of unpredictable (combinations of) attack patterns. In addition to signature-based scanning, most vulnerability scanners attempt to simulate the reconnaissance attack patterns used by attackers to “probe” software for exposed, exploitable vulnerabilities.

Vulnerability scanners can be either network-based or host-based. Network-based scanners target the software from a remote platform across the network, while host-based scanners must be installed on the same host as the target. Host-based scanners generally perform more sophisticated analyses, such as verification of secure configurations, while network-based scanners more accurately simulate attacks that originate outside of the targeted system (i.e., the majority of attacks in most environments). Vulnerability scanning is fully automated, and the tools typically have the high false positive rates that typify most pattern-matching tools, as well as the high false-negative rates that plague other signature-based tools. It is up to the tester to both configure and calibrate the scanner to minimize both false positives and false negatives to the greatest possible extent, and to meaningfully interpret the results to identify real vulnerabilities and weaknesses. As with virus scanners and intrusion detection systems, the signature repositories of vulnerability scanners need to be updated frequently. For testers who wish to write their own exploits, the open source Metasploit Project publishes black hat information and tools for use by penetration testers, intrusion detection system signature developers, and researchers. The disclaimer on the Metasploit website is careful to state:

“This site was created to fill the gaps in the information publicly available on various exploitation techniques and to create a useful resource for exploit developers. The tools and information on this site are provided for legal security research and testing purposes only.”


Black Box Security Testing

Black box testing is generally used when the tester has limited knowledge of the system under test or when access to source code is not available. Within the security test arena, black box testing is normally associated with activities that occur during the pre-deployment test phase (system test) or on a periodic basis after the system has been deployed.
Black box security tests are conducted to identify and resolve potential security vulnerabilities before deployment or to periodically identify and resolve security issues within deployed systems. They can also be used as a “badness-ometer” [McGraw 04] to give an organization some idea of how bad the security of their system is. From a business perspective, organizations conduct black box security tests to conform to regulatory requirements, protect confidentially and proprietary information, and protect the organization’s brand and reputation.
Fortunately, a significant number of black box test tools focus on application security related issues. These tools concentrate on security related issues including but not limited to:

  • Input checking and validation
  • SQL insertion attacks
  • Injection flaws
  • Session management issues
  • cross-site scripting attacks
  • Buffer overflow vulnerabilities
  • Directory traversal attacks

Benefits and Limitations of Black Box Testing

As previously discussed, black box tests are generally conducted when the tester has limited knowledge of the system under test or when access to source code is not available. On its own, black box testing is not a suitable alternative for security activities throughout the software development life cycle. These activities include the development of security-based requirements, risk assessments, security-based architectures, white box security tests, and code reviews. However, when used to complement these activities or to test third-party applications or security-specific subsystems, black box test activities can provide a development staff crucial and significant insight regarding the system’s design and implementation.

Black box tests can help development and security personnel to:

• Identify implementation errors that were not discovered during code reviews, unit tests, or security white box tests
• Discover potential security issues resulting from boundary conditions that were difficult to identify and understand during the design and implementation phases
• Uncover security issues resulting from incorrect product builds (e.g., old or missing modules/files)
• Detect security issues that arise as a result of interaction with underlying environment (e.g., improper configuration files, unhardened OS and applications)

“White Box” Techniques for security testing

White box” tests and analyses, by contrast with “black box” tests and analyses, are performed on the source code. Specific types of white box analyses and tests include:

Static Analysis

It is known as “code review,” static analysis analyses source code before it is compiled, to detect coding errors, insecure coding constructs, and other indicators of security vulnerabilities or weaknesses that are detectable at the source code level. Static analyses can be manual or automated. In a manual analysis, the reviewer inspects the source code without the assistance of tools.

In an automated analysis, a tool (or tools) is used to scan the code to locate specific “problem” patterns (text strings) defined to it by the analyst via programming or configuration, which the tool then highlights or flags. This enables the reviewer to narrow the focus of his/her manual code inspection to those areas of the code in which the patterns highlighted or flagged in the scanner’s output appear.

Direct Code Analysis

Direct code analysis extends the static analysis by using tools that focus not on finding individual errors but on verifying the code’s overall conformance to a set of predefined properties, which can include security properties such as noninterference and separability, persistent_BNDC, noninference, forward-correct ability, and nondeductibility of outputs.

Property-Based Testing

The purpose of property-based testing is to establish formal validation results through testing. To validate that a program satisfies a property, the property must hold whenever the program is executed. Property-based testing assumes that the specified property captures everything of interest in the program and assumes that the completeness of testing can be measured structurally in terms of source code. The testing only validates the specified property, using the property’s specification to guide dynamic analysis of the program. Information derived from the specification determines which points in the program need to be tested and whether each test execution is correct. A metric known as Iterative Contexts Coverage uses these test execution points to determine when testing is complete. Checking the correctness of each execution together with a description of all the relevant executions results in the validation of the program with respect to the property being tested, thus validating that the final product is free of any flaws specific to that property.

Source Code Fault Injection

A form of dynamic analysis in, which the source code is “instrumented” by inserting changes, then compiling and executing the instrumented code to observe the changes in state and behavior, which emerge when the instrumented portions of code are executed. In this way, the tester can determine and even quantify how the software reacts when it is forced into anomalous states, such as those triggered by intentional faults. This technique has proved particularly useful for detecting the incorrect use of pointers and arrays, and the presence of dangerous calls and race conditions. Fault injection is a complex testing process and thus tends to be limited to code that requires very high assurance.

Fault Propagation Analysis

This involves two techniques for fault injection of source code: extended propagation analysis and interface propagation analysis. The objective is not only to observe individual state changes that result from a given fault but to trace how those state changes propagate throughout a fault tree that has been generated from the program’s source code. Extended propagation analysis entails injecting a fault into the fault tree and then tracing how the fault propagates through the tree. The tester then extrapolates outward to predict the impact a particular fault may have on the behavior of the software module or component, and ultimately the system, as a whole. In interface propagation analysis, the tester perturbs the states that propagate via the interfaces between the module or component and its environment. To do this, the tester injects anomalies into the data feeds between the two levels of components and then watches to see how the resulting faults propagate and whether any new anomalies result. Interface propagation analysis enables the tester to determine how a failure in one component may affect its neighboring components.

Pedigree Analysis

While not a security testing technique in itself, the detection of pedigree indicators in open source code can be helpful in drawing attention to the presence of components that have known vulnerabilities, pinpointing them as high-risk targets in need of additional testing. This is a fairly new area of code analysis that was sparked by concerns regarding open source licensing and intellectual property violations.

Dynamic Analysis of Source Code

Dynamic analysis involves both the source code and the binary executable generated from the source code. The compiled executable is run and “fed” a set of sample inputs while the reviewer monitors and analyzes the data (variables) the program produces as a result. With this better understanding of how the program behaves, the analyst can use a binary-to-source map to trace the location in the source code that corresponds to each point of execution in the running program, and more effectively locate faults, failures, and vulnerabilities. In The Concept of Dynamic Analysis:

  1. Coverage concept analysis
  2. Frequency spectrum analysis.

Coverage concept analysis attempts to produce “dynamic control flow invariants” for a set of executions, which can be compared with statically derived invariants in order to identify desirable changes to the test suite that will enable it to produce better test results.

Frequency spectrum analysis counts the number of executions of each path through each function during a single run of the program. The reviewer can then compare and contrast these separate program parts in terms of higher versus lower frequency, the similarity of frequencies, or specific frequencies.

The first analysis reveals any interactions between different parts of the program, while the second analysis reveals any dependencies between the program parts. The third analysis allows the developer to look for specific patterns in the program’s execution, such as uncaught exceptions, assert failures, dynamic memory errors, and security problems. A number of dynamic analysis tools have been built to elicit or verify system-specific properties in the source code, including call sequences and data invariants.


[1]Assuring Software Security Through Testing, White, Black and Somewhere in Between by Mano Paul



[1] State-of-the-Art Report (SOAR) July 31, 2007 – Information Assurance Technology Analysis Center (IATAC) Data and Analysis Center for Software (DACS)

[1] Gu Tian-yang, Shi Yin-sheng, and Fang You-yuan “Research on Software Security Testing” – World Academy of Science, Engineering and Technology 69 2010


Software security testing in SDLC?

When to perform Software security analysis and tests?

Most of software security practitioners would agree that the common practice of postponing security analysis and tests after the software implementation phase and even after it has been deployed (i.e., during its acceptance phase), makes it extremely difficult to address in a cost-effective, timely manner any vulnerabilities and weaknesses discovered during the analysis and testing process.

Figure [1] illustrates the relation between cost and time in security testing process which may be doubled or tripled due to lack of this testing coverage during its proper time.

image image

Figure [1][i] Security testing cost vs. time – cost of fixing software bugs

Source: OSSTMM – Open Source Security Testing Methodology Manual

So, Security testing involves in software developing life cycle to ensure the implementation of security requirements. It is worth mentioning that Security testing is not a phase only in SDLC but it involves also in many system components and processes as illustrated in figure [2] below.


Figure [2]Security in system components

Source: OSSTMM – Open Source Security Testing Methodology Manual

So, each component of the system has different methodologies and techniques to assure the security, while our focus here on software development life cycle. The Figure [3] and Figure[4] below illustrate the security testing existence at SDLC


Figure [3] Describes each of the formal methods activities in the diagram, indicating the SDLC phases to which each activity pertains

Source: Information Assurance Technology Analysis Center (IATAC)


Figure [4] Security testing in SDLC – 7 touchpoints

[ii]Figure [4] illustrates the software security touchpoints (a set of best practices) and shows how software practitioners can apply the touchpoints to the various software artifacts produced during software development.

These best practices first appeared as a set in 2004 in IEEE Security & Privacy magazine. Since then, they have been adopted (and in some cases adapted) by the U.S. government in the National Cyber Security Task Force report, by Cigital, by the U.S. Department of Homeland Security, and by Ernst and Young.

So here in table [1] a range of security reviews, analysis, and tests can be mapped to the different software life cycle phases starting with the requirements phase:

Life Cycle Phase Reviews/tests
Requirements Security review of requirements and abuse/misuse cases
Architecture/Product Design Architectural risk analysis (including external reviews)
Detailed Design Security review of the design. Development of test plans, including security tests.
Coding/Unit Testing Code review (static and dynamic analysis), white box testing
Assembly/Integration Testing Black box testing (fault injection, fuzz testing)
System Testing Black box testing, vulnerability scanning
Distribution/Deployment Penetration testing (by software testing expert), vulnerability scanning, impact analysis of patches
Maintenance/support (Feedback loop into previous phases), impact analysis of patches and updates
Security testing in software test plan

The security test plan should be included in the overall software test plan, and should define:

  1. Security test cases or scenarios (based on misuse and abuse cases)
  2. Test data, including attack patterns
  3. Test oracle
  4. Test tools (white box and black box, static and dynamic)
  5. Analysis to be performed to interpret, correlate, and synthesize the results from the various tests and outputs from the various tools.

The security test plan should acknowledge that the security assumptions that were valid when the software’s requirements were specified; will probably have changed by the time the software is deployed. The threat environment in which the software will actually operate is unlikely to have remained static. New threats and attack patterns are continually emerging. Also, emerging has new versions of non-developmental components and patches to those components. All these changes have the potential to invalidate at least some of the security assumptions under which the original requirements were specified.



What is Software security testing?

Secure Software

In Software industry, Most of clients have a main requirement which is “we want the system to be secured”. Security is a non functional property of system, the main goal for securing the system to make this system dependable. So, we can depend on this system and it can perform its excepted functions as required and specified.

Therefore, it is mandatory to run the security testing procedures to insure that we can depend on this system, but we need also to consider some functional requirements on writing requirements specifications document that helps to obtain this goal.

Definition of Security testing

“Security testing is a process to determine that an information system protects data and maintains functionality as intended” Wikipedia

We can summarize that we need security testing on the following:

  1. Information and access security. Security tests help to find out loopholes that can cause loss of important information or allow intruder into the systems.
  2. System stability and availability. Security testing helps to improve the system and finally helps it to work for longer time (or it will work without hassles for the estimated time).
  3. System integrity. If involved on the early stages of development life cycle, security testing allows eliminating possible flaws in system design and implementation. We need to consider security aspects in architecture phase.
  4. Economic efficiency. It’s much cheaper to prevent the possible problem rather than to strive for resolving it and its consequences.

The main objective of software security analysis and testing is the verification that the software exhibits the following properties and behaviors:

  1. Its behavior is predictable and secure.
  2. It exposes no vulnerabilities or weaknesses (ideally it contains no vulnerabilities or weaknesses, exposed or not).
  3. Its error and exception handling routines enable it to maintain a secure state when confronted by attack patterns or intentional faults.
  4. It satisfies all of its specified and implicit nonfunctional security requirements.
  5. It does not violate any specified security constraints.
  6. As much of its runtime-interpretable source code and byte code as possible has been obscured or obfuscated to deter reverse engineering.

Software Security testing

What did they say about Software security testing?

“Over 70 percent of security vulnerabilities exist at the application layer, not the network layer” Gartner.

Hacking has moved from a hobbyist pursuit with a goal of notoriety to a criminal pursuit with a goal of money” Counterpane Internet Security.

“64 percent of developers are not confident in their ability to write secure applications” Microsoft Developer Research.

“Losses arising from vulnerable web applications are significant and expensive – up to $60 billion annually”IDC/IBM Systems Sciences Institute.

“If 50 percent of software vulnerabilities were removed prior to production use, enterprise configuration management and incident response costs would be reduced by 75 percent each.”Gartner.

General Statistics

The figures below illustrate that lake of software security allows data breaches. These breaches have been categorized by sector, this has been illustrated in figure [1] and figure [2].


Figure (1) Data breaches that could lead to identity theft and identities exposed, by sector
Source: Based on data provided by OSF DataLossDB (due to rounding, percentages may not total 100 percent)


Figure (2) Average number of identities exposed per data breach, by notable sector
Source: Based on data provided by OSF DataLossDB

The figures below illustrate that lake of software security allows data breaches. At these figures, these breaches have been categories by cause.


Figure (3) Data breaches that could lead to identity theft and identities exposed, by cause
Source: Based on data provided by OSF DataLossDB (due to rounding, percentages may not total 100 percent)


Figure (4) Average number of identities exposed per data breach, by cause
Source: Based on data provided by OSF DataLossDB

Below figure illustrates type of information exposed in deliberate breaches.


Figure (5) Type of information exposed in deliberate breaches
Source: Based on data provided by OSF DataLossDB (due to rounding, percentages may not total 100 percent)

The Impact of unsecured application

The impact of unsecured software application can vary from organization to another based on importance of the system and its related data as following:

The potential impact is LOW if:

The loss of confidentiality, integrity, or availability could be expected to have a limited adverse effect on organizational operations, assets, or individuals.

The potential impact is MODERATE if:

The loss of confidentiality, integrity, or availability could be expected to have a serious adverse effect on organizational operations, assets, or individuals.

The potential impact is HIGH if:

The loss of confidentiality, integrity, or availability could be expected to have a severe or catastrophic adverse effect on organizational operations, assets, or individuals.

Types of application need to have security testing

  • Web-applications
  • Applications with sensitive commercial or personal information
  • Payment and statistic systems
  • Applications, sensitive to data distortion
  • Social applications
  • Applications with expensive licensing

The need for security testing

It is important to recognize that there are three key quality components to software assurance as shown in Figure [6]; reliability, resiliency, and recoverability.

  • Reliable software is that which functions as needed by the end user.
  • Resilient software is that which is able to withstand the attempts of an attacker to compromise confidentiality, and/or impact integrity, or availability (CIA).
  • Recoverable software is software that is capable of restoring itself or being restored to expected normal operations when it has failed in its reliability or resiliency.


Figure (6) [i] Software Quality component

Most commonly, when software is said to be of “quality”, it essentially means that the software is working as designed and expected. This is primarily a consideration of software functionality and not its assurance capabilities. However the reliability aspect of software quality today, it is also imperative to take into account the security of the software. This two-pronged approach to software quality testing ensures that software is not only reliable but resilient to withstand attacks that impact CIA.

Therefore, Security testing is necessary because it has a distinct relationship with software quality. The software may meet quality requirements related to the functionality and performance, but it does not necessary mean that this software is secure. The inverse, however, is true.

So, software called secure when it is software with added resiliency, thus software of higher quality, for example, when the “Add to cart” button on a web page is clicked and the selected product is added to the cart (functionality) in less than the expected two-second requirement (performance). It can be urged that this software met the reliability quality requirements as established by the business, but if the software is not tested for security, there is no guarantee that the product code that is added to the cart has not been tampered by an unauthorized user.

Moreover, poor architecture and implementation of the web application cannot assure the CIA aspect of software assurance.

This was an introduction for software security testing; I will add more posts to illustrate more about the definition of Security testing, its relation with the software developing life cycle, and its techniques.


[i]Assuring Software security through testing, White, Black and Somewhere in between by Mano Paul