Business logic attacks provide more information, higher impact, and perfect working comfort. Each logic attack is unique since it has to exploit a function or feature that is specific to each application.
PFB an example of a logic attack.
If you purchase 10 units of the same item, the online store will give you a big discount.
- The discount is applied when an attacker adds 10 items to the cart.
- The attacker took 9 items from the cart.
- The attacker gets the discount because the system hasn't checked if there are still enough items in the cart.
Many developers forget to implement
sanity-checks like these in many places because this example is easy to enforce. There are more
complicated logic attacksout there.
Logic attacks are very common and are unique to each application and feature. The properties make it hard to find using automated tools.
BLST helps service providers adjust their operations and react quickly to changing demand with its application-centered algorithms.
In terms of learning the traffic, this algorithm is unique. Their Artificial Penetration Tester product is able to work with any kind of web application because it learns the results after simulating attacks on the application.
A number of methodology products are combined into one to save time and effort.
BLST provides a solution that helps the App Sec and DevOps teams understand the business logic attack flows and allows them to work more efficiently and ship better and more secure code faster. BLST uses an
artificial intelligence model that learns the usage of users in the system and begins to simulations of business logic attack flows before the system hits production.
Early in the integration phase, Attacker mimics business logic attack flows. It can help you find business logic attack flows that could lead to the exposure of sensitive data.
The BLST Decider can detect a wide range of threats. The Decider can differentiate between
normal and abnormal behavior in the system and give you the ability to observe each abnormal case that has happened.
BLST compares the logs from the runtime to the OpenAPI specification to see if there are differences between the specification and what's running in production. A clear image for every use case suggests an easy path to quick remediation.
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