Peakhour.IO - TLS

JA4 and JA4+ Network Fingerprinting

2023-10-25T13:00:00+11:00

JA4+ is the name FoxIO uses for a family of network fingerprinting methods. JA4 itself is the TLS ClientHello method. It builds on lessons from JA3, but the wider family also contains separate methods for servers, HTTP, certificates, TCP, SSH and other observations.

JA4 and JA4+

JA4 produces an a_b_c value. Its readable a section records selected connection properties and counts. The b and c sections are truncated SHA-256 values derived from normalised ClientHello fields. Analysts can compare selected components, such as JA4_ac, when the complete fingerprint is too narrow for the question being asked. Other JA4+ methods have their own inputs and specifications; they should not be treated as extra fields inside core JA4.

JA4+ consists of various components:

JA4: TLS Client
JA4S: TLS Server Response
JA4H: HTTP Client
JA4L: Light Distance/Location
JA4X: X509 TLS Certificate
JA4SSH: SSH Traffic

For a more thorough breakdown, the JA4 blog provides the announcement and describes the fingerprints.

JA4+ brings useful improvements, but a few aspects and quirks deserve closer attention.

What sorting changes

JA4 sorts cipher identifiers and most extension identifiers before hashing them. This was especially useful after Chrome began permuting TLS extension order. Sorting puts those permutations back into one cohort. It also discards the order as evidence. That is the trade-off: a more stable identifier retains less information about how the ClientHello was serialised.

Where investigation matters, retain the raw JA4 form as well as the compact value. JA4_r exposes the normalised cipher, extension and signature-algorithm lists, which makes a difference easier to inspect.

The overview of TLS fingerprinting provides a more in-depth explanation of how a TLS signature is formed.

Chrome's change was intended to stop servers and middleboxes from depending on one fixed extension order. In our extension-randomisation analysis, the number of order-sensitive TLS fingerprints rose sharply after the rollout. Sorting reduced that artificial fragmentation. It did not make the resulting value a client identity, and it did not preserve every distinction in the original handshake.

JA3 and Mercury took different paths

Before digging further into JA4+'s features and limitations, it helps to separate two related lineages. The original JA3 established a portable TLS fingerprint that was easy to share and match. Cisco Mercury developed a richer protocol representation and a separate destination-context classification system. Mercury is not a predecessor in the JA3-to-JA4 naming line. Our history of the two lineages explains where their work overlaps and where it does not.

Implementation differences still matter

While sharing signatures through SHA is appealing, it has limits, most notably potential compatibility issues. As Fastly noted, differences in the implementation can be hidden behind the SHA hash, causing issues when searching for and correlating signatures between different services. Record the implementation and version that generated a value; a shared format name does not prove that two sensors handled every field identically.

Check the method, implementation and licence

The official JA4+ repository contains the current specifications and implementations. Check the licence for the individual method before adopting it: core JA4 is BSD-3-Clause, while most other JA4+ methods use the FoxIO Licence and place additional conditions on commercial use.

For a field-level example rather than a format summary, our same-ClientHello lab records JA3, JA4, JA4_r and Mercury NPF output from one packet and pins the implementations that generated them.

A Secure Internet

2023-08-16T00:00:00+10:00

Here at Peakhour, we track browser security changes because they affect how sites are delivered and how users experience warnings. Google Chrome has made another move towards encrypted and authenticated traffic by expanding HTTPS-First Mode. Here is what Chrome unveiled on August 16, 2023, and what it means for HTTPS by default.

Automatic Upgrades to HTTPS

Chrome aims to make HTTPS the standard protocol by automatically upgrading all HTTP navigations to HTTPS. Even if you click a link explicitly declaring HTTP, Chrome will try HTTPS first. If the upgrade fails because of an invalid certificate or another issue, Chrome will fall back to HTTP.

The change is part of an experiment in Chrome version 115. It does not protect against active network attackers, but it does shift more everyday traffic away from passive eavesdropping and towards HTTPS as the default.

Warning on Insecurely Downloaded Files

Chrome is also adding warnings before users download high-risk files over insecure connections. Downloaded files can contain malicious code that compromises a computer. The warning gives users a clearer signal before they proceed, while still allowing the download if they accept the risk. The rollout of these warnings is expected to start in mid-September.

Expanding HTTPS-First Mode Protections

Chrome's longer-term goal is to enable HTTPS-First Mode for all users. It is expanding those protections in several areas:

Enabling HTTPS-First Mode for users in Google's Advanced Protection Program who are also signed into Chrome.
Planning to enable HTTPS-First Mode by default in Incognito Mode for a more secure browsing experience.
Experimenting with automatically enabling HTTPS-First Mode on sites frequently accessed over HTTPS.
Exploring automatically enabling HTTPS-First Mode for users who rarely use HTTP.

Try it Out

For users who want to try HTTPS upgrading or insecure download warnings before the full rollout, Chrome has provided options in the browser's settings to enable these features.

Peakhour's HTTPS Redirection Feature at the Edge

At Peakhour, HTTPS redirection is a practical edge control. It helps enforce encrypted and authenticated connections before a request reaches the origin.

When a user attempts to access a site over HTTP, our edge identifies the unsecured connection. Instead of allowing that connection through, we redirect the request to the HTTPS version of the site.

Enhanced Security: By enforcing HTTPS, data transmitted between your website and your users is encrypted and protected from potential attackers.
Compliance with Best Practices: This feature aligns with industry standards and recent browser policies, including Chrome's push towards HTTPS-first mode.
User Trust: A secure connection gives users a clearer reason to trust the site, improving the user experience and potentially supporting higher conversion rates.

We also offer options for customisation, allowing you to set specific rules and behaviours for how HTTP requests are handled and redirected to HTTPS. Peakhour's HTTPS redirection feature at the edge is a small control with a clear job: move HTTP traffic onto HTTPS automatically, protect users, and keep sites aligned with current browser expectations.

Final Thoughts

Chrome's push towards a secure-by-default web is another step towards a fully encrypted and authenticated internet. It also matches the way Peakhour thinks about everyday security controls: enforce the basics at the edge, and make the safe path the default.

Chrome's changes may require developers, enterprises, and users to adapt. The direction is still clear: less plain HTTP, more HTTPS by default, and fewer silent insecure paths. If your organisation is reviewing its HTTP handling, Peakhour can help you apply the right redirects and edge rules.

Chrome's TLS Extension Randomisation Experiment

2023-02-02T13:00:00+11:00

Transport Layer Security (TLS) fingerprinting is a commonly used technique for identifying client processes. To reduce the risk of server and middlebox fingerprinting of Chrome's current ClientHello and to make the TLS ecosystem more resilient to changes, Google Chrome ran an experiment to randomise a portion of the TLS fingerprint. This experiment was included in Chrome version 108, which was released on December 8, 2022. You can read the status of the current experiment on the chrome status site

The aim of this experiment was to make it more difficult for server implementers to fingerprint Chrome and assume specific implementation behaviour from a fixed extension order. By randomly ordering extensions (subject to the pre_shared_key constraint in the RFC), Chrome hoped to reduce the risk of server and middlebox fixating on details of its current ClientHello.

The above graph correlates to the Chrome experiment and subsequent release of the feature. The number of unique TLS signatures dramatically increased.

From Peakhour data, we can see a large number of unique fingerprints appearing since the date of the experiment, making it very difficult to identify the Chrome network stack by a TLS fingerprint alone. However, an analysis by David McGrew, a Cisco Fellow, cast doubt on the effectiveness of this experiment. In his article, McGrew proposed a lexicographical sorting of TLS extensions and found that 98.8% of signatures were unique after sorting. He argues that the canonical ordering of the TLS extensions in the TLS fingerprint can achieve nearly the same level of entropy as randomising them and still be effective at client identification. Furthermore, he claims that the RFC should be amended to state that extensions SHOULD be sent in an ordered fashion in the ClientHello packet. McGrew also highlights the potential dangers of allowing unordered extension lists, as it could create a \"subliminal channel\" that could be used for tracking or transmitting information. Let's now graph, over the same period, the number of TLS signatures with TLS extension sorting.

The above graph correlates to David's assertion that sorting TLS extensions has minimal impact on TLS fingerprinting.

It appears that David's assertion is correct: sorting TLS extensions has minimal impact on the number of unique TLS fingerprints. Let's now look at in-the-wild Chrome 109 data:

Hashed sorted TLS Fingerprint	Unique unsorted TLS fingerprints	Browser	Version	% of clients	% of hits
3241796329	2	Chrome Mobile WebView	109	6.14	1.01
3313291307	8566	Chrome	109	1.83	1.54
1537819294	26587	Chrome Mobile	109	6.05	4.64
3944870384	1	Chrome Mobile iOS	109	4.31	5.35
3241796329	51594	Chrome Mobile	109	16.9	14.43
1537819294	121346	Chrome	109	15.66	20.7
3241796329	156405	Chrome	109	35.52	37.79

It's interesting that the experiment does not run on WebView.

Final Thoughts

While Chrome's experiment may have reduced the risk of server and middlebox fingerprinting of Chrome's current ClientHello, it seems that randomising TLS extensions alone is not enough to prevent TLS fingerprinting, and may be a useful indicator that it is The Real Chrome.

This experiment became one of the reasons newer formats normalise extension order. Our JA3, JA4 and Mercury lab shows exactly where each format keeps, sorts or discards ClientHello detail. The accompanying history of the two TLS fingerprinting lineages explains why Cisco Mercury and JA4 made related but different design choices.

The remaining research question is whether discarded order ever helps distinguish an imitator or evasive client. Does TLS fingerprint canonicalisation hide useful attacker variation? defines the labelled corpus and holdout study needed to answer it without confusing uniqueness with detection accuracy.

TLS Fingerprinting

2023-02-02T13:00:00+11:00

What is Fingerprinting?

Fingerprinting is a technique that may be used to identify the specific device, web browser, and operating system making a request, regardless of what the client says in its user-agent header. By helping organisations identify and characterise the attributes of a client's connection, fingerprinting can improve network security and help protect against malicious traffic.

Fingerprinting can also refer to techniques for following or uniquely identifying individual users across the web. That is a separate set of techniques and is not discussed in this article.

Transport Layer Security (TLS) Fingerprinting determines the specific characteristics of a client's TLS implementation by examining the initial TLS handshake packet, known as the "Client Hello." This packet contains fields and parameters such as supported cipher suites, extensions, and the client's preferred order of those parameters, which can be used to create a unique "fingerprint" of the client's TLS implementation.

Why is it used?

Fingerprinting has several uses, including bot protection, DDoS protection, and client identification. By identifying and characterising the attributes of a client's connection, fingerprinting can improve network security and help protect against malicious traffic.

How does TLS Fingerprinting work?

TLS Fingerprinting examines the initial TLS handshake packet, known as the "Client Hello". The Client Hello packet is sent by the client during the initial phase of the TLS handshake, which establishes a secure connection between the client and the server. It contains information about the client's preferred encryption methods, extensions, and parameters, including:

Protocol Version: The version of the TLS protocol desired by the client.
Random: A 32-byte random value generated by the client, used in key generation and derivation.
Session ID: An optional session identifier for resuming a previous session.
Cipher Suites: A list of supported encryption algorithms, ordered by preference.
Compression Methods: A list of supported compression algorithms, ordered by preference.
Extensions: Optional extensions that can negotiate additional parameters, such as Server Name Indication (SNI) and Elliptic Curve Supported (ECS).

The Client Hello packet is central to the operation and security of the TLS connection because it provides information the server uses to select encryption algorithms and parameters. The packet also enables the client and server to negotiate an appropriate encryption method for their communication. The Client Hello's variable content, based on the TLS version, library, cipher suites, extensions, and settings supported by the client, makes it a strong candidate for fingerprinting.

Common components used to create a TLS fingerprint include:

Cipher Suites: The order of cipher suites supported by the client.
Extensions: Supported extensions included in the Client Hello packet, such as SNI and ECS.
TLS Point Formats: Encoding of cryptographic parameters in a format that can be transmitted as part of the TLS protocol, used in elliptic curve cryptography (ECC).
TLS Curves: The specific elliptic curves used in ECC, a type of public-key cryptography used in the TLS protocol.

TLS fingerprinting has been a topic of research for several years, with a number of tools and techniques developed from that work. Notable examples include JA3, developed by John Althouse, Jeff Atkinson, and Josh Atkins of Salesforce, which uses a hash of the client's SSL/TLS parameters as a unique identifier for tracking and analysing SSL/TLS traffic. Another tool, Mercury by David McGrew and Blake Anderson, can be used to fingerprint client connections and identify the device, operating system, and application making the connection.

TLS fingerprinting has a variety of uses, including bot protection, DDoS protection, malware identification and client identification. By enabling organisations to identify and characterise the attributes of a client's TLS implementation, TLS fingerprinting can improve network security and help protect against malicious traffic.

In production, TLS fingerprints are most useful when combined with IP intelligence and residential proxy detection, rather than treated as a standalone verdict.

Representation of a TLS Fingerprint

A TLS fingerprint is commonly represented as a string or hash that summarises the important components of the Client Hello packet. The most common components used to create a TLS fingerprint include the supported cipher suites, extensions, and TLS point formats. The cipher suites are represented as a list of hexadecimal values in the order they are presented by the client, while extensions and point formats are represented as a list of hexadecimal values or a unique identifier.

Raw JA3 signatures are represented by the following fields, which are then hashed with MD5:

SSLVersion, Cipher, SSLExtension, EllipticCurve, EllipticCurvePointFormat

An example raw signature is:

 771,4865-4867-4866-49195-49199-52393-52392-49196-49200-49162-49161-49171-49172-156-157-47-53,0-23-65281-10-11-35-16-5-34-51-43-13-45-28-21,29-23-24-25-256-257,0

An MD5 hash is then applied, resulting in the final signature.

579ccef312d18482fc42e2b822ca2430

Mercury signatures are represented by:

"tls/1" (TLS_Version) (TLS_Ciphersuite) [ Extension* ]

An example signature is:

tls/1/
(0303)
(130213031301c02cc030009fcca9cca8ccaac02bc02f009ec024c028006bc023c0270067c00ac0140039c009c0130033009d009c003d003c0035002f00ff)
[
   (0000)
   (000a000c000a001d0017001e00190018)
   (000b000403000102)
   (000d0030002e040305030603080708080809080a080b080408050806040105010601030302030301020103020202040205020602)
   (0016)
   (0017)
   (0023)
   (002b0009080304030303020301)
   (002d00020101)
   (0033)
]

Hash Functions for Representing TLS Fingerprints

Hashing algorithms, such as MD5, are commonly used to create a unique representation of a TLS fingerprint. These hash functions take the client's TLS parameters as input and produce a fixed-length output, which serves as a unique identifier for the client. The hash value can be compared against a database of known TLS fingerprints to help determine the identity of the client.

Other techniques for representing TLS fingerprints include base64 encoding of the client's TLS parameters, such as in the Mercury fingerprint.

Challenges with TLS fingerprinting

TLS fingerprinting is not a foolproof method for identifying clients and their attributes. It has several limitations that need to be considered.

False Positives: TLS fingerprinting relies on the assumption that the client's Client Hello packet uniquely identifies a connecting process by its TLS implementation. However, it is possible for a client to alter the Client Hello packet by customising TLS parameters, which affects the Client Hello packet and can result in a false positive identification. This makes it important to use multiple methods for identifying clients. For example, Mercury takes into account destination ports to add additional context.
False Negatives: While TLS fingerprinting can identify many different clients and their attributes, it is not capable of identifying all clients. Some clients may have a unique or unusual TLS implementation that cannot be accurately fingerprinted. Additionally, some clients may actively attempt to evade fingerprinting by customising TLS parameters or using tools to anonymise their connections.
Forging of TLS Fingerprints: It is possible for attackers to deliberately forge or modify the information contained in their Client Hello packet to appear as a different client. This makes it difficult for fingerprinting tools to accurately identify the true identity of a client and can be used for malicious purposes, such as evading security measures or disguising the origin of an attack.
Incomplete Data: TLS fingerprinting is limited by the information contained in the Client Hello packet, which may not contain all of the necessary data to accurately identify a client. For example, a client may not send a full list of supported cipher suites or extensions, may use a modified version of the TLS protocol that is not recognised by the fingerprinting tool, or the fingerprint may not be present in available databases.

Different fingerprinting implementations can result in different hashes for the same TLS connection, even though the underlying SSL/TLS protocol remains unchanged. This happens due to the various algorithms, parameters, and representations used by different fingerprinting tools.

For instance, implementation differences when generating the TLS fingerprint may cause hashes found in public databases to be inconsistent with a locally generated hash.

Final Thoughts

Be aware of the limitations and differences between fingerprinting implementations, and choose the right tool and representation for your specific use case. Standardising the representation of fingerprints and using common hash algorithms can help avoid confusion and improve interoperability between databases.

Setting Up A Chia Hobby Farm

2021-04-30T13:00:00+10:00

Here at Peakhour, when we're not making websites faster and more secure, we like new tech and we like a good scheme. We ran Seti@home while at uni, and mined some bitcoin back in its early days (unfortunately we don’t have them anymore). Just recently we decided to set up a Chia farm, not the super-food Chia, but the new crypto coin Chia!

What is Chia?

Chia is not just a cryptocurrency; it is a brand new blockchain and smart transaction platform that implements the first new Nakamoto consensus algorithm since Bitcoin. It was invented by the engineer behind BitTorrent, Bram Cohen, who set out to address the shortcomings of Bitcoin.

The Chia network is set to officially launch on May 3rd, and the crypto world is going crazy getting ready.

I thought Bitcoin was great, what’s wrong with it?

The major flaws that Chia sets out to address are:

The environmental impact

Without getting too technical, Bitcoin relies on very intensive computations to verify transactions (Proof of work). These computations are carried out by 'miners' who are rewarded for their efforts from an ever decreasing pool of possible bitcoin. As the blockchain gets older, the verification gets harder, and as a result the Bitcoin network is now consuming as much electricity as a mid-sized country like Argentina. Huge mining operations have been set up in China, and some even have dedicated power plants. One poster child for the environmental impacts of bitcoin is an Australian startup looking to reopen a decommissioned coal power plant to power its mining operations.

Possibility of manipulation

The huge energy requirements have led to massive server farms in cool regions near cheap electricity, concentrating mining in the hands of a few large players. This centralisation opens up Bitcoin to the possibility of manipulation as anyone with 50% of the network can effectively change the blockchain.

How does Chia address these issues with Bitcoin?

Chia has implemented a new consensus algorithm called proof of space and time. It relies on unused hard disk space, which lots of people have and can use free of charge. Again, without getting too technical, 'Farmers' seed unused space on their hard drive/SSD with 'plots' of cryptographic numbers. When verifying transactions, the network issues a challenge to the farmers, who then scan their plots for the closest answer. The farmer passes this answer back to a server on the network known as a 'timelord'. The farmer with the closest answer is rewarded with a coin.

The more 'plots' a farmer has, the higher the chance of winning a coin.

Setting up the Farm

We got excited about the idea of Chia being the next big thing and decided to hitch a ride on the bandwagon. We had a spare old computer lying around, so we decided to fill it up with as much storage as we could find and farm some Chia!

To set up a farm you need as much space for plots as you can get your hands on. The speed of this space is not critical, so you can use spinning drives. We found 12-terabyte NAS drives to be the sweet spot for bang for buck, and opted for 4x Seagate Ironwolf NAS drives from Scorptec. (Note: they’ve gone up $40 since we bought them!)

Seeding the plots, however, is VERY disk intensive, so you need speedy and reliable SSDs. Since they don't have moving parts you'd think that SSDs would be very reliable, but just like spinning drives, they wear out and eventually die. SSDs come with a TBW (Terabytes Written) rating which estimates the amount of writes you can do before the drive will die. Popular consumer SSDs like a 500GB Samsung EVO 870 have a TBW rating of 300. Chia recommends getting server-grade SSDs that have ratings into the Petabytes, but of course they come with a price to match.

We were limited by the age of our available motherboard, so we could only choose from SATA3-compatible drives. Appropriate enterprise SSDs were also unavailable, so in the end we settled on 500GB Seagate Firecuda 120s that are rated at 700 TBW (also from Scorptec). We decided on two so we could double the plotting rate.

Now we had our hands on the drives, we just had to install everything. Within a few hours of transferring components and wiring it up we were good to go and started plotting.

Our Chia Farm!

Final Thoughts

Our old hardware limits the speed of the SSDs and therefore the number of plots we generate. We're managing around 10 plots a day and will need close to 500 before we’ve filled the available storage.

When we bought our equipment (28th April) the chia calculator showed that we’d be earning around a coin a day when fully plotted. However, with the official launch of Chia imminent, the network has exploded in growth, passing 1 Exabyte (1000 Terabytes) just one day ago. It's now up to 1.68 Exabytes! So unfortunately our estimated time to a coin is down to one every 7 days. That’s still pretty good though, and if Chia does end up supplanting Bitcoin we might just make back the setup costs. It has been a fun exercise, even if we did spend too long on it, and if it does end up being a flash in the pan we can always use the drives for something else….