Detecting and exploiting limit overrun race conditions with Turbo Intruder — Portswigger

In addition to providing native support for the single-packet attack in Burp Repeater, we’ve also enhanced the Turbo Intruder extension to support this technique. You can download the latest version from the BApp Store | Karthikeyan Nagaraj

n addition to providing native support for the single-packet attack in Burp Repeater, we’ve also enhanced the Turbo Intruder extension to support this technique. You can download the latest version from the BApp Store.

Turbo Intruder requires some proficiency in Python, but is suited to more complex attacks, such as ones that require multiple retries, staggered request timing, or an extremely large number of requests.

To use the single-packet attack in Turbo Intruder:

1. Ensure that the target supports HTTP/2. The single-packet attack is incompatible with HTTP/1.
2. Set the engine=Engine.BURP2 and concurrentConnections=1 configuration options for the request engine.
3. When queueing your requests, group them by assigning them to a named gate using the gate argument for the engine.queue() method.
4. To send all of the requests in a given group, open the respective gate with the engine.openGate() method.

def queueRequests(target, wordlists):
    engine = RequestEngine(endpoint=target.endpoint,
                            concurrentConnections=1,
                            engine=Engine.BURP2
                            )
    
    # queue 20 requests in gate '1'
    for i in range(20):
        engine.queue(target.req, gate='1')
    
    # send all requests in gate '1' in parallel
    engine.openGate('1')

For more details, see the race-single-packet-attack.py template provided in Turbo Intruder's default examples directory.

Lab: Bypassing rate limits via race conditions | Web Security Academy

Hidden multi-step sequences

In practice, a single request may initiate an entire multi-step sequence behind the scenes, transitioning the application through multiple hidden states that it enters and then exits again before request processing is complete. We’ll refer to these as “sub-states”.

If you can identify one or more HTTP requests that cause an interaction with the same data, you can potentially abuse these sub-states to expose time-sensitive variations of the kinds of logic flaws that are common in multi-step workflows. This enables race condition exploits that go far beyond limit overruns.

For example, you may be familiar with flawed multi-factor authentication (MFA) workflows that let you perform the first part of the login using known credentials, then navigate straight to the application via forced browsing, effectively bypassing MFA entirely.

Note

If you’re not familiar with this exploit, check out the following lab from one of our other topics:

Authentication vulnerabilities: 2FA simple bypass

The following pseudo-code demonstrates how a website could be vulnerable to a race variation of this attack:

 session['userid'] = user.userid
    if user.mfa_enabled:
        session['enforce_mfa'] = True
        # generate and send MFA code to user
        # redirect browser to MFA code entry form

As you can see, this is in fact a multi-step sequence within the span of a single request. Most importantly, it transitions through a sub-state in which the user temporarily has a valid logged-in session, but MFA isn’t yet being enforced. An attacker could potentially exploit this by sending a login request along with a request to a sensitive, authenticated endpoint.

We’ll look at some more examples of hidden multi-step sequences later, and you’ll be able to practice exploiting them in our interactive labs. However, as these vulnerabilities are quite application-specific, it’s important to first understand the broader methodology you’ll need to apply in order to identify them efficiently, both in the labs and in the wild.

Methodology

To detect and exploit hidden multi-step sequences, we recommend the following methodology, which is summarized from the whitepaper Smashing the state machine: The true potential of web race conditions by PortSwigger Research.

1 — Predict potential collisions

Testing every endpoint is impractical. After mapping out the target site as normal, you can reduce the number of endpoints that you need to test by asking yourself the following questions:

• Is this endpoint security critical? Many endpoints don’t touch critical functionality, so they’re not worth testing.
• Is there any collision potential? For a successful collision, you typically need two or more requests that trigger operations on the same record. For example, consider the following variations of a password reset implementation:

With the first example, requesting parallel password resets for two different users is unlikely to cause a collision as it results in changes to two different records. However, the second implementation enables you to edit the same record with requests for two different users.

2 — Probe for clues

To recognize clues, you first need to benchmark how the endpoint behaves under normal conditions. You can do this in Burp Repeater by grouping all of your requests and using the Send group in sequence (separate connections) option. For more information, see Sending requests in sequence.

Next, send the same group of requests at once using the single-packet attack (or last-byte sync if HTTP/2 isn’t supported) to minimize network jitter. You can do this in Burp Repeater by selecting the Send group in parallel option. For more information, see Sending requests in parallel. Alternatively, you can use the Turbo Intruder extension, which is available from the BApp Store.

Anything at all can be a clue. Just look for some form of deviation from what you observed during benchmarking. This includes a change in one or more responses, but don’t forget second-order effects like different email contents or a visible change in the application’s behavior afterward.

3 — Prove the concept

Try to understand what’s happening, remove superfluous requests, and make sure you can still replicate the effects.

Advanced race conditions can cause unusual and unique primitives, so the path to maximum impact isn’t always immediately obvious. It may help to think of each race condition as a structural weakness rather than an isolated vulnerability.

Multi-endpoint race conditions

Perhaps the most intuitive form of these race conditions are those that involve sending requests to multiple endpoints at the same time.

Think about the classic logic flaw in online stores where you add an item to your basket or cart, pay for it, then add more items to the cart before force-browsing to the order confirmation page.

A variation of this vulnerability can occur when payment validation and order confirmation are performed during the processing of a single request.

Aligning multi-endpoint race windows

When testing for multi-endpoint race conditions, you may encounter issues trying to line up the race windows for each request, even if you send them all at exactly the same time using the single-packet technique.

This common problem is primarily caused by the following two factors:

• Delays introduced by network architecture — For example, there may be a delay whenever the front-end server establishes a new connection to the back-end. The protocol used can also have a major impact.
• Delays introduced by endpoint-specific processing — Different endpoints inherently vary in their processing times, sometimes significantly so, depending on what operations they trigger.

Fortunately, there are potential workarounds to both of these issues.

Connection warming

Back-end connection delays don’t usually interfere with race condition attacks because they typically delay parallel requests equally, so the requests stay in sync.

It’s essential to be able to distinguish these delays from those caused by endpoint-specific factors. One way to do this is by “warming” the connection with one or more inconsequential requests to see if this smoothes out the remaining processing times. In Burp Repeater, you can try adding a GET request for the homepage to the start of your tab group, then using the Send group in sequence (single connection) option.

If the first request still has a longer processing time, but the rest of the requests are now processed within a short window, you can ignore the apparent delay and continue testing as normal.

If you still see inconsistent response times on a single endpoint, even when using the single-packet technique, this is an indication that the back-end delay is interfering with your attack. You may be able to work around this by using Turbo Intruder to send some connection warming requests before following up with your main attack requests.

Abusing rate or resource limits

If connection warming doesn’t make any difference, there are various solutions to this problem.

Using Turbo Intruder, you can introduce a short client-side delay. However, as this involves splitting your actual attack requests across multiple TCP packets, you won’t be able to use the single-packet attack technique. As a result, on high-jitter targets, the attack is unlikely to work reliably regardless of what delay you set.

Single-endpoint race conditions

Sending parallel requests with different values to a single endpoint can sometimes trigger powerful race conditions.

Consider a password reset mechanism that stores the user ID and reset token in the user’s session.

In this scenario, sending two parallel password reset requests from the same session, but with two different usernames, could potentially cause the following collision:

Note the final state when all operations are complete:

• session['reset-user'] = victim
• session['reset-token'] = 1234

The session now contains the victim’s user ID, but the valid reset token is sent to the attacker.

Note

For this attack to work, the different operations performed by each process must occur in just the right order. It would likely require multiple attempts, or a bit of luck, to achieve the desired outcome.

Email address confirmations, or any email-based operations, are generally a good target for single-endpoint race conditions. Emails are often sent in a background thread after the server issues the HTTP response to the client, making race conditions more likely.