Différences

Ci-dessous, les différences entre deux révisions de la page.

--- en:cs:web_applications_threats_modeling [2017/01/27 14:45] – [Threats class: A1 injection] fraggle
+++ en:cs:web_applications_threats_modeling [2021/12/27 18:25] (Version actuelle) – modification externe 127.0.0.1
@@ Ligne 8: / Ligne 8: @@
 ===== Threats class: A1 injection =====
-Each web applications has a finite set of input parameters being in forms, URL parameters, ... called $ \mathcal{I} = \{i_{1},\dots,i_{n}\}, \, n \in \mathbb{N} $.\\
+Each web applications has a finite set of visibles parameters being in forms, URL parameters, ... called $ \mathcal{P} = \{p_{1},\dots,p_{n}\}, \, n \in \mathbb{N} $.\\
-All input parameters are typed: $ i_{1} \, {\longmapsto} \, t_{3}; \dots ; \, i_{n} \, {\longmapsto} \, t_{7}, \, t_{i} $ being chosen in a finite set of type called
+All parameters are not typed: the HTTP protocol only transport text. But we might consider the langage associated to each parameters $ p_{i} $ to ease the future data injection.
-$ \mathcal{T} =
-   \left \{
-   \begin{array}{r c l}
-      t_{1}  & = & integer \\
-      t_{2}  & = & string \\
-      \vdots & & \vdots \\
-      t_{k}  & = & type_{k}
-   \end{array}
-   \right . \quad k \in \mathbb{N}. $
-We will consider the cartesian product $ \mathcal{I}\times\matchcal{T} $ in the future.
+We will consider the set $ \mathcal{P} $ in the future.
-__Question:__ Does it matter if it's a form input or an URL parameter or ... ?
+__Question:__ Does it matter if it's a form input parameter or an URL parameter or ... ?
 Let's describe the processus to assess A1 threats in a web application.
-  * Phase one: determine $ \mathcal{I}\times\matchcal{T} $
+  * Phase one: determine $ \mathcal{P} $
-It will be depend on the approach chosen.\\
+It will depend on the approach chosen.\\
 If it's a black box testing, it will be based on URLs scanning, pages content scanning, ....\\
 If it's static code analysis, it will be based on detection of code pattern.\\
-Each inputs' type shall be inferred.
+In case of code static analysis, each visible parameters' type shall be inferred.
-We might also consider the location of each input.\\
+We might also consider the location of each parameters.\\
-__Question__: When the location information of inputs will be useful ?\\
+__Question__: When the location information of parameters will be useful ?\\
-(If it's useful, it will bring in a new finite set $ \mathcal{L} $ which will contain $ \{l_{1},\dots,l_{p}\}, \, p \in \mathbb{N} $ where $ l_{i} $ is the location of $ i_{i} $ which might not be unique !)
+(If it's useful, it will bring in a new finite set $ \mathcal{L} $ which will contain $ \{l_{1},\dots,l_{p}\}, \, p \in \mathbb{N} $ where $ l_{i} $ is the location of $ p_{i} $ which might not be unique !)
   * Phase two: determine data pattern to inject
-It will of course not be a blind and random data building like fuzzing, data should be intelligently crafted depending on the inputs type and probably location. The building of the set of data patterns is challenging.\\
+It will of course not be a blind and random data building like fuzzing, data should be carefully crafted depending on the parameters langage and probably location. The building of the set of data patterns is challenging.\\
 For now, we only know it is finite.
-One way to build it is to start with an alphabet and some syntactic rules to combine each elements in the alphabet in a meaningful fashion for security.
+One way to build it is to start with an alphabet and some syntactic rules to combine each element in the alphabet in a meaningful fashion for security.
-  * Phase three: inject sensibly the data patterns in all inputs
+  * Phase three: inject sensibly the data patterns in all visible parameters
 Inject.