Authors:
(1) Diwen Xue, University of Michigan;
(2) Reethika Ramesh, University of Michigan;
(3) Arham Jain, University of Michigan;
(4) Arham Jain, Merit Network, Inc.;
(5) J. Alex Halderman, University of Michigan;
(6) Jedidiah R. Crandall, Arizona State University/Breakpointing Bad;
(7) Roya Ensaf, University of Michigan. Table of Links Abstract and 1 Introduction 2 Background & Related Work 3 Challenges in Real-world VPN Detection 4 Adversary Model and Deployment 5 Ethics, Privacy, and Responsible Disclosure 6 Identifying Fingerprintable Features and 6.1 Opcode-based Fingerprinting 6.2 ACK-based Fingerprinting 6.3 Active Server Fingerprinting 6.4 Constructing Filters and Probers 7 Fine-tuning for Deployment and 7.1 ACK Fingerprint Thresholds 7.2 Choice of Observation Window N 7.3 Effects of Packet Loss 7.4 Server Churn for Asynchronous Probing 7.5 Probe UDP and Obfuscated OpenVPN Servers 8 Real-world Deployment Setup 9 Evaluation & Findings and 9.1 Results for control VPN flows 9.2 Results for all flows 10 Discussion and Mitigations 11 Conclusion 12 Acknowledgement and References Appendix 6.3 Active Server Fingerprinting We explore the feasibility of identifying an OpenVPN server through active probing. Typically, OpenVPN servers respond to a client reset with an explicit server reset, thereby giving away their identity. However, most commercial providers now have adopted tls-auth or tls-crypt options [50]. These options add an additional HMAC signature—signed by a pre-shared key—to every control channel packet for integrity verification, including the initial reset packets. With either of these options enabled, an OpenVPN server would not respond to an unauthenticated client reset with a server reset, but would instead drop such packets without further processing. The presence of such HMAC mechanism increases the complexity of doing active probing: it effectively makes OpenVPN servers “probe-resistant” [13] by remaining silent when probed by an unauthenticated client. In fact, similar HMAC mechanisms are used by more popular “probe-resistant” proxies, such as obfs4 [33]. However, unlike obfs4 which waits for a server-specific random delay before dropping an unauthenticated connection, OpenVPN always immediately closes the connection if a valid HMAC cannot be located. We design our probes to leverage this protocol-specific behavior, and as a result, we manage to fingerprint OpenVPN servers even if they do not respond throughout our probing cycles. The key concept is that although the application may not respond to probing, an attacker may still be able to fingerprint application-specific thresholds at the TCP level, such as timeouts or RST thresholds, as demonstrated by Frolov et al. [13]. We use two datasets in this section to help with designing probes. ZMap Set: to construct a realistic non-VPN endpoints dataset, we use ZMap to scan each of the 65,535 TCP ports over the entire IPv4 space, limiting results for each port to 200 endpoints (with the specific port open), resulting in over 13 million endpoints. Censys Set: We query the Censys.io [10] database for hosts with TCP port 1194/OpenVPN open. Next, we probe each endpoint with a typical OpenVPN Client Reset and group endpoints that respond with explicit Server Resets. This results in 180,858 hosts known to be OpenVPN endpoints (with “tls-auth” disabled). 6.3.1 Base Probes We design probes exploiting a behavior associated with how OpenVPN packetizes TCP streams. When OpenVPN operates over TCP, it needs to split the continuous stream into discrete OpenVPN packets. Figure 5 presents a high-level abstraction of this process. The most relevant parts are: a buffer is allocated in memory to reassemble fragments of OpenVPN packets encapsulated in TCP streams. The length N for the next OpenVPN packet is extracted from the first two bytes of the header (see Figure 3), and the routine keeps reading N additional bytes before it returns the reassembled packet to the caller. This means that an OpenVPN packet will not be parsed and checked for syntax and encryption errors until all its parts arrive at the server. Based on this behavior, we design two sequential probes to trigger an OpenVPN server into different code paths—which result in different connection timeouts— and measure the time elapsed before the server responds or terminates the connection. As shown in Table 1, Base Probe 1 carries a typical 16-byte OpenVPN Client Reset, while Base Probe 2 has the same payload with the last byte stripped off. The assumption is since our two probes only differ in one byte, most non-OpenVPN servers will respond to our probes in a similar way. However, for an OpenVPN server with HMAC enabled, the connection sending the first probe will be dropped immediately because the OpenVPN packet is reassembled and a valid HMAC cannot be located. The second probe will not receive an immediate response, as the server will wait for an additional byte to arrive for reassembly. The connection will stay idle until a server specific handshake timeout has passed, after which the connection will be dropped. As such, the first probe will be dropped at the decryption routine, while the second probe will be dropped at the packet reassembly routine, both labeled red in Figure 5. 6.3.2 Additional Probes The two probes, although useful, are limited and there may be other protocols with behaviors similar to OpenVPN. After using both to probe the ZMap Set, we still identify a handful of services that respond similarly to OpenVPN servers, such as Microsoft WBT Server (3389), Microsoft Message Queuing (1801), and Erlang Port Mapper Daemon (4369). We design additional probes based on the fact that OpenVPN validates packet length and will drop connections sending invalid length without waiting for the next packet to be reassembled. Here, packet length refers to the length declared by the first two bytes of an OpenVPN header (see Figure 3), rather than the TCP packet length. A “valid” length is in the range of [1, max_len], where max_len is derived from the server’s MTU configurations. For instance, default TUN MTU of 1500 bytes, combined with overheads (crypto IV, packet length, etc.), results in a max_len of 1627 bytes. In this case, probes whose first two bytes have a decimal value greater than 1627 (0x06,0x5B) will be dropped immediately. We also design probes leveraging the way a Linux server closes a TCP connection. When a TCP connection terminates, the operating systems at both ends typically complete a FIN 4-way handshake. However, previous work has found that if a connection is closed with unread bytes in buffer, Linux will send a RST packet [13]. A server’s “RST Threshold” is defined as the minimum number of bytes needed to send to the server to trigger a RST. We determine the RST threshold distribution for both ZMap Set and Censys Set. As shown in Figure 6, the vast majority of OpenVPN servers have a RST threshold around 1550-1660 bytes, corresponding to buffers allocated with typical MTU configurations. In contrast, over 97% of random ZMap endpoints have a RST threshold less than 500 or greater than 4000. We therefore construct an additional probe with 2,000 random bytes, which we expect over 98% of legitimate OpenVPN servers and less than 3% of random servers to respond to with RST packets. It is worth noting that the majority of “typical” HTTP, TLS, and SSH servers have already been filtered out by our base probes, so endpoints that respond at this stage are likely sharing the port with another service, thus warranting manual analysis (e.g., checking TLS certificate). While these three services are what we commonly observed, there may be instances where other services are running along with OpenVPN. This could lead to false negatives. Table 1 lists all probes and the expected behaviors from an OpenVPN server. An evaluation process is shown in Appendix Figure 11. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Diwen Xue, University of Michigan; (2) Reethika Ramesh, University of Michigan; (3) Arham Jain, University of Michigan; (4) Arham Jain, Merit Network, Inc.; (5) J. Alex Halderman, University of Michigan; (6) Jedidiah R. Crandall, Arizona State University/Breakpointing Bad; (7) Roya Ensaf, University of Michigan. Authors: Authors: (1) Diwen Xue, University of Michigan; (2) Reethika Ramesh, University of Michigan; (3) Arham Jain, University of Michigan; (4) Arham Jain, Merit Network, Inc.; (5) J. Alex Halderman, University of Michigan; (6) Jedidiah R. Crandall, Arizona State University/Breakpointing Bad; (7) Roya Ensaf, University of Michigan. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Related Work 2 Background & Related Work 3 Challenges in Real-world VPN Detection 3 Challenges in Real-world VPN Detection 4 Adversary Model and Deployment 4 Adversary Model and Deployment 5 Ethics, Privacy, and Responsible Disclosure 5 Ethics, Privacy, and Responsible Disclosure 6 Identifying Fingerprintable Features and 6.1 Opcode-based Fingerprinting 6 Identifying Fingerprintable Features and 6.1 Opcode-based Fingerprinting 6.2 ACK-based Fingerprinting 6.2 ACK-based Fingerprinting 6.3 Active Server Fingerprinting 6.3 Active Server Fingerprinting 6.4 Constructing Filters and Probers 6.4 Constructing Filters and Probers 7 Fine-tuning for Deployment and 7.1 ACK Fingerprint Thresholds 7 Fine-tuning for Deployment and 7.1 ACK Fingerprint Thresholds 7.2 Choice of Observation Window N 7.2 Choice of Observation Window N 7.3 Effects of Packet Loss 7.3 Effects of Packet Loss 7.4 Server Churn for Asynchronous Probing 7.4 Server Churn for Asynchronous Probing 7.5 Probe UDP and Obfuscated OpenVPN Servers 7.5 Probe UDP and Obfuscated OpenVPN Servers 8 Real-world Deployment Setup 8 Real-world Deployment Setup 9 Evaluation & Findings and 9.1 Results for control VPN flows 9 Evaluation & Findings and 9.1 Results for control VPN flows 9.2 Results for all flows 9.2 Results for all flows 10 Discussion and Mitigations 10 Discussion and Mitigations 11 Conclusion 11 Conclusion 12 Acknowledgement and References 12 Acknowledgement and References Appendix Appendix 6.3 Active Server Fingerprinting We explore the feasibility of identifying an OpenVPN server through active probing. Typically, OpenVPN servers respond to a client reset with an explicit server reset, thereby giving away their identity. However, most commercial providers now have adopted tls-auth or tls-crypt options [50]. These options add an additional HMAC signature—signed by a pre-shared key—to every control channel packet for integrity verification, including the initial reset packets. With either of these options enabled, an OpenVPN server would not respond to an unauthenticated client reset with a server reset, but would instead drop such packets without further processing. The presence of such HMAC mechanism increases the complexity of doing active probing: it effectively makes OpenVPN servers “probe-resistant” [13] by remaining silent when probed by an unauthenticated client. In fact, similar HMAC mechanisms are used by more popular “probe-resistant” proxies, such as obfs4 [33]. However, unlike obfs4 which waits for a server-specific random delay before dropping an unauthenticated connection, OpenVPN always immediately closes the connection if a valid HMAC cannot be located. We design our probes to leverage this protocol-specific behavior, and as a result, we manage to fingerprint OpenVPN servers even if they do not respond throughout our probing cycles. The key concept is that although the application may not respond to probing, an attacker may still be able to fingerprint application-specific thresholds at the TCP level, such as timeouts or RST thresholds, as demonstrated by Frolov et al. [13]. We use two datasets in this section to help with designing probes. ZMap Set: to construct a realistic non-VPN endpoints dataset, we use ZMap to scan each of the 65,535 TCP ports over the entire IPv4 space, limiting results for each port to 200 endpoints (with the specific port open), resulting in over 13 million endpoints. Censys Set: We query the Censys.io [10] database for hosts with TCP port 1194/OpenVPN open. Next, we probe each endpoint with a typical OpenVPN Client Reset and group endpoints that respond with explicit Server Resets. This results in 180,858 hosts known to be OpenVPN endpoints (with “tls-auth” disabled). 6.3.1 Base Probes 6.3.1 Base Probes We design probes exploiting a behavior associated with how OpenVPN packetizes TCP streams. When OpenVPN operates over TCP, it needs to split the continuous stream into discrete OpenVPN packets. Figure 5 presents a high-level abstraction of this process. The most relevant parts are: a buffer is allocated in memory to reassemble fragments of OpenVPN packets encapsulated in TCP streams. The length N for the next OpenVPN packet is extracted from the first two bytes of the header (see Figure 3), and the routine keeps reading N additional bytes before it returns the reassembled packet to the caller. This means that an OpenVPN packet will not be parsed and checked for syntax and encryption errors until all its parts arrive at the server. Based on this behavior, we design two sequential probes to trigger an OpenVPN server into different code paths—which result in different connection timeouts— and measure the time elapsed before the server responds or terminates the connection. As shown in Table 1, Base Probe 1 carries a typical 16-byte OpenVPN Client Reset, while Base Probe 2 has the same payload with the last byte stripped off. The assumption is since our two probes only differ in one byte, most non-OpenVPN servers will respond to our probes in a similar way. However, for an OpenVPN server with HMAC enabled, the connection sending the first probe will be dropped immediately because the OpenVPN packet is reassembled and a valid HMAC cannot be located. The second probe will not receive an immediate response, as the server will wait for an additional byte to arrive for reassembly. The connection will stay idle until a server specific handshake timeout has passed, after which the connection will be dropped. As such, the first probe will be dropped at the decryption routine, while the second probe will be dropped at the packet reassembly routine, both labeled red in Figure 5. 6.3.2 Additional Probes 6.3.2 Additional Probes The two probes, although useful, are limited and there may be other protocols with behaviors similar to OpenVPN. After using both to probe the ZMap Set, we still identify a handful of services that respond similarly to OpenVPN servers, such as Microsoft WBT Server (3389), Microsoft Message Queuing (1801), and Erlang Port Mapper Daemon (4369). We design additional probes based on the fact that OpenVPN validates packet length and will drop connections sending invalid length without waiting for the next packet to be reassembled. Here, packet length refers to the length declared by the first two bytes of an OpenVPN header (see Figure 3), rather than the TCP packet length. A “valid” length is in the range of [1, max_len], where max_len is derived from the server’s MTU configurations. For instance, default TUN MTU of 1500 bytes, combined with overheads (crypto IV, packet length, etc.), results in a max_len of 1627 bytes. In this case, probes whose first two bytes have a decimal value greater than 1627 (0x06,0x5B) will be dropped immediately. We also design probes leveraging the way a Linux server closes a TCP connection. When a TCP connection terminates, the operating systems at both ends typically complete a FIN 4-way handshake. However, previous work has found that if a connection is closed with unread bytes in buffer, Linux will send a RST packet [13]. A server’s “RST Threshold” is defined as the minimum number of bytes needed to send to the server to trigger a RST. We determine the RST threshold distribution for both ZMap Set and Censys Set. As shown in Figure 6, the vast majority of OpenVPN servers have a RST threshold around 1550-1660 bytes, corresponding to buffers allocated with typical MTU configurations. In contrast, over 97% of random ZMap endpoints have a RST threshold less than 500 or greater than 4000. We therefore construct an additional probe with 2,000 random bytes, which we expect over 98% of legitimate OpenVPN servers and less than 3% of random servers to respond to with RST packets. It is worth noting that the majority of “typical” HTTP, TLS, and SSH servers have already been filtered out by our base probes, so endpoints that respond at this stage are likely sharing the port with another service, thus warranting manual analysis (e.g., checking TLS certificate). While these three services are what we commonly observed, there may be instances where other services are running along with OpenVPN. This could lead to false negatives. Table 1 lists all probes and the expected behaviors from an OpenVPN server. An evaluation process is shown in Appendix Figure 11. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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Enhancing OpenVPN Detection: Combining Fingerprinting and Active Probing

Unveiling OpenVPN: Fingerprinting with ACK Patterns and Timing Analysis

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Active Probing: How OpenVPN Servers Are Fingerprinted Through Unique Behaviors

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