Wednesday, May 29, 2013

False Negative Detection Support in IronWASP

NOTE: Before reading this post I would strongly recommended reading the introduction post that covers basics of web security scanner functioning and False Negatives

False Negative is not a word you hear often in the security industry and for good reason too. False Negatives eventually go on to become 0-days and they are the manifestation of the limitations in our knowledge, tools, techniques, processes and skills.

Every single day there is a constant attack against this limitation, it could be in the form of a researcher coming up with a new technique or a tool developer introducing a better algorithm or something similar. But the unknown is infinite and for that reason False Negatives shall remain an incurable condition.
Having said that, we can get better results against False Negatives by casting a wider net. The wider, the better. This is where Anomaly detection come into play, it's as wide as it gets. An Anomaly is simply a deviation from the normal. The basis of Anomaly based detection is that any factor that causes an anomaly to occur is potentially a problem that requires being looked into.
In the context of web security scanning, an anomaly is the application behaving differently than it normally would. This could be in the form of the application returning a page with different text than it normally would, or the application taking a longer or shorter time duration to respond than it normally would etc.
A web security scanner sends a wide range of payloads as part of its many vulnerability checks, these payloads could include almost the entire spectrum of what is typically considered bad input. There is a very high probability then that these payloads would trigger anomalies to occur in sections of the application that are vulnerable. If these anomalies are identified and manually investigated then the tester could potentially identify vulnerabilities that the signature based scanner misses out.
As you can see the concept of Anomaly detection is fairly straight forward and I am sure I am not the only scanner developer to think of this. In fact, during a random conversation with @skeptic_fx I discovered that he had been quietly toying with this idea in his head. This then begs the question, why has nobody implemented this already? The answer to that lies in how the existing scanners are designed. They are designed to provide only a Boolean output as the result of a vulnerability scan - 'I found a vulnerability' or 'I didn't find a vulnerability'. Because of this, users are also conditioned to expect only this kind of a result from the scanner. Anomaly detection does not naturally fit in to this design and retrofitting it would negatively affect the user's perception of the scanner. The user might look at the thousands of anomalies reported and declare that the scanner produces too many False Positives!

IronWASP has a completely different approach to what results a scan must produce. Of Course you have the regular list of identified vulnerabilities reported like all the other scanners but it doesn't end there. The scanner also has a treasure trove of information about how the application behaved for the different payloads that it sent, this information can take away the need for any additional manual or automated fuzzing and bring down the testing effort and time by a significant margin. IronWASP has been the only scanner to make this information available to the user in a structured form suitable for analysis, the others have been wasting this data like the Cheese manufactures throwing away Whey!.

Right from its very first version IronWASP has been logging this information under the Scan Trace section along with every single request and response associated with the scan. With every newer version progressive improvements have been made to the Scan Trace section, the next natural step in this trajectory is automated anomaly detection from the Scan Trace data.
Let's now see how this is implemented.

IronWASP's False Negative Detection Support:

The latest version of IronWASP has a feature named 'Payload Effect Analyzer'. Once an automated scan is completed on an application, the Payload Effect Analyzer can be launched to start an automated analysis of the scan trace data. For each payload that was sent by the scanner, the analyzer compares the corresponding response (payload response) against the response that was received for same request without the payload (normal response). Any variations between these two responses is most likely caused by the payload that was sent by the scanner. The analyzer identifies all these variations or anomalies from the scan trace data and lists them for manual analysis by the user.
The analyzer looks for variations in the following factors between the Normal Response and the Payload Response:

1) Response Code: Checks if the HTTP Status Codes of both the responses are different
2) Response Content: Checks if the 'payload response' has text that is missing from the 'normal response'. There is a possibility that this new text in the 'payload response' might be some kind of an exception detail or error message triggered by the injected payload, so these are picked up by the analyzer.
By default there must at least be 20 characters of text exclusively found in the 'payload response' for it to be reported. This number can however be modified by the user before starting the analysis.
3) Response Time: Checks if there is significant difference in the time taken for both the responses to be received from the server.
By default significant difference is considered as one of these two:
  • If the difference in the response time of the both the responses is more than 1000 milliseconds
  • If the response time of one response is more than 10 times the response time of the other response.
These are the default settings, they can be modified by the user before starting the analysis.
4) Response Headers: Checks if any HTTP headers present in one response is missing from the other response.
5) Response Set-Cookie values: Checks if any cookie values set by one response are different from the other response.

In addition to these, the analyzer can also detect the presence of certain keywords in the payload response that were not present in the normal response. By default the keywords that are searched for are 'error', 'exception', 'not allowed', 'unauthorized', 'blocked', 'filtered', 'attack', 'unexpected', 'sql', 'database' and 'failed'. The user can update this list before starting the analysis.
Now that we have looked at the theory behind all this, let's see how this feature can be used.
As mentioned earlier, before the Payload Effect Analyzer can be launched, an automated vulnerability scan of the target application must be performed. This article explains how to start an automated scan using IronWASP.
After the scan is complete head to the 'Scan Trace' section located inside the 'Automated Scanning' section and click on the 'Launch Payload Effect Analyzer' button. This will open a the analyzer in a new window.
Clicking the 'Launch Payload Effect Analyzer' button opens the Payload Effect Analyser in a new window

The scan trace selection settings available in the Payload Effect Analyser window, the default settings cover the entire scan trace database
The analyzer window gives a few options to narrow down which section of the scan trace must be included in the analysis. The user can provide the range of scan trace ids which must be analyzed and/or specify scan trace generated by which vulnerability checks must be included. If the user is not satisfied with the payloads that the existing vulnerability checks send then a new vulnerability check can be created which sends payloads provided by the user. Creating this new check does not require any programming knowledge and can be done in less than a minute by making use of the 'Active Plugin Creation Assistant' utility. This utility can be accessed from the 'Coding Assistant' section of the 'Dev Tools' menu.

Before starting the analysis if the user wants to change the default configuration settings for the analyzer then it can be done by clicking on the 'Show Config Panel' link.
Options to configure the Payload Effect Analyzer
The analysis can now be started by clicking on the 'Start Analysis' button. As the analysis happens any anomalies detected are immediately reported to the user.
Analysis is in progress, anomalies identified so far are already reported
The results list contains brief information about why a particular entry was added. It provides the id of the scan trace on which the anomaly has been detected and following six fields explain why it was reported.
1) Code Variation: this fields indicates if any of the payloads caused the application to return a response with a different HTTP status code than the normal response.
2) Keywords Inserted: this field indicates if the payloads caused the application to return a response that contained any of the keywords in the analysis configuration.
3) Body Variation: if any of the payloads caused a significant change in the response content then this field is populated with the number of characters of difference between the payload response and normal response. If more than one payload caused a significant difference in response content then the maximum change is shown here.
4) Set-Cookie Variations: this field indicates whether any of the payloads caused a difference in the Set-Cookie HTTP header section of the responses.
The differences that are picked up are:
  • Normal response did not have a Set-Cookie header but payload response has this header
  • Normal response had a Set-Cookie header but the payload response does not have this header.
  • Both the normal response and payload response has Set-Cookie headers but their values are different.
5) Headers Variation: this field indicates whether any of the payload caused a difference in the HTTP header section of the responses.
The differences that are picked up are:
  • Header present in normal response is missing in the payload response
  • Header that was not present in normal response is present in payload response
6) Time Variation: if any of the payloads caused a significant change in the time taken for the response to be returned then this field is populated with the number of milliseconds of difference between the payload response and normal response. If more than one payload caused a significant difference in response time then the maximum change is shown here.
Clicking on any of these result entries shows more details about the selected entry. Let's pick one and analyze more.
Summary of selected result
The summary section explains what anomalies were discovered in this particular trace entry. In this cases there is variation in the response code, content, headers, cookie values and response times. Also occurrences of two of the keywords have been detected.
Let's probe further into one of these anomalies. The summary section says that some payload caused the application to return a response that was having two new cookie values - 'amUserInfo' and 'amUserId'. To know more about this let's go to the 'Set-Cookie Variations' tab.
Set-Cookie variations section of the selected result. This sections shows the exact payload sent and the corresponding Set-Cookie variation
Here you can see the exact payloads that triggered the introduction of these new cookies. The Log ID of the corresponding request and response is also shown here so we can take a look at them. The logs associated with this scan trace entry can be viewed by clicking on the 'Load Selected Trace Entry in Trace Viewer' button, this opens the selected trace in a separate window.
Scan Trace Viewer showing all the requests and responses associated with this scan
In the Trace Viewer we can locate the exact log using the log id observed earlier. Clicking on it shows the associated request and response below. By looking at the request we learn that this is actually a login request (the application being scanned in this case is which is a test site maintained by the IBM AppScan team).
The associated response shows that the application is redirecting us to the welcome page which is only accessible after a successful authentication. And the cookies that are being set are to maintain our new authenticated session. Based on this we can deduce that it is possible to bypass the authentication in the login page using SQL injection. IronWASP does report that the login section is vulnerable to SQL Injection but it does not detect the authentication bypass. But by using the Payload Effect Analyzer we are able to pick this up quite easily.
If you want to check how different this particular response is from the responses received for some of the other payloads then we can do that in three simple steps.
Step 1: Change the 'Click Action' to 'Select Log', this enables the selection of multiple logs.
Step 2: Click on the two logs which must be compared. A check mark be placed on the selected logs.
Step 3: After selecting exactly two logs click on the 'Diff Selected Sessions' button. This will open a new window that will highlight all the differences in the requests and responses between the selected logs.
Once 'Click Action' is set to 'Select Log' it is possible to select multiple logs and do a diff on the requests and responses.
Differences in the requests from the selected logs is highlighted.
Differences in the responses from the selected logs is highlighted
This was a simple scenario just to illustrate how the analyzer works. In real world this feature can help you identify anomalies that you might miss out even during manual testing. And if this feature is used properly then you can remove the monotonous repeated manual payload injection work from your tests. And instead reserve manual injection for only cases where the analyzer detects anomalies.

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