China Bound and Unbound: History in the Making - An Early Returnees Account

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We then localized where with respect to our measurement system in the network topology the GFW operates, as follows. This packet capture shows our localization results. In contrast, no legitimate server reply accompanied the injected malicious reply from the GC. We ran further testing, where we retransmitted our request to Baidu over the same connection, and with the same sequence numbers, after we received a malicious response. We observed Baidu responding as normal to the retransmitted request.

Section 2: The Firewall & The Cannon: Separate Systems, Significant Similarities

We also checked whether the GFW and GC might share the same injector device, 14 but found no evidence that they do. In particular, from a given TCP source port, we sent one request designed to trigger GC injection, followed by a request designed to trigger GFW injection. We repeated the experiment from a number of source ports.

We found that for our path, the GC acted on traffic between hop 17 and hop 18 , the same link we observed as responsible for the GFW. We also checked two separate servers in China whose traffic the GC targets to observe whether the GC existed along with the Great Firewall on multiple network paths. From our measurement system outside of China, we examined the path to both For When we probed an IP address adjacent to the Baidu server For a given source IP address and port, the GC only examines the first data packet sent when deciding whether to inject a reply.

To avoid examining subsequent packets requires remembering which connections it has examined in a flow cache.


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Each probe had a different source port. We sent probes, each with the request split across three packets so 1, packets total. The GC ignored each probe. We then sent a final single-packet probes, each containing a complete, valid request for a targeted resource, to confirm normal GC operation. As expected, the GC injected malicious content in some cases, seemingly based on its probabilistic decision-making process. We attempted to completely fill the GC flow cache by sending packets to the Baidu server with different source IP addresses and ports, while probing to see whether the entries that we previously added had now expired.

Our attempt suggests that at least in some cases, the GC flow cache between our test system and the Baidu server supports up to around 16, entries for a single sending IP address. Censorship systems generally operate in a deterministic fashion: they aim to block all content that matches the target criteria. The GC, on the other hand — at least for this particular attack — appears to act probabilistically, and ignores most of the traffic it could act on.

In one test , it completely ignored all traffic from one of four measurement IP addresses, and on the three other measurement IP addresses it only responded to requests out of an initial 30, from the three 1. When we succeeded in completely filling the flow cache, subsequently injected packets occurred for different source ports than in the initial test. If the GC only intercepted a subset of flows to the target IP address, we would expect subsequent injections to appear for the same flows, since most schemes to probabilistically select flows for interception such as hashing the connection 4-tuple would select the same set of flows the second time around.

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We determined that the GC uses a separate flow cache for different source IP addresses, and that packets injected from different source IP addresses have distinct TTL side-channels. This finding suggests a load-balanced architecture similar to the GFW, where packets are routed to GC nodes based on source IP address.


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We then sent traffic alternating from four measurement IP addresses in an attempt to fill a 16, entry cache. This attempt did not manage to fill the cache, suggesting that the GC not only processed the different source IP addresses with different injection elements, but did so using different flow caches. As stated before, one of the four source IP addresses never received any injected replies.

The staff of GreatFire. To keep our analysis tractable, we examined a sample of the data from March 18th GMT to March 19th GMT, as seen from two of the three most commonly seen backend servers. For each hour, we selected 30MB of compressed logs for each server. For any IP address that did not result in a definite geolocation using this tool 31 addresses , we looked up the address manually using the iplocation. Note that 8, China, however, accounted for only 18 requests. As expected, the GC injected malicious content in some cases, seemingly based on its probabilistic decision-making process.

We attempted to completely fill the GC flow cache by sending packets to the Baidu server with different source IP addresses and ports, while probing to see whether the entries that we previously added had now expired. Our attempt suggests that at least in some cases, the GC flow cache between our test system and the Baidu server supports up to around 16, entries for a single sending IP address. Censorship systems generally operate in a deterministic fashion: they aim to block all content that matches the target criteria.

The GC, on the other hand — at least for this particular attack — appears to act probabilistically, and ignores most of the traffic it could act on. In one test , it completely ignored all traffic from one of four measurement IP addresses, and on the three other measurement IP addresses it only responded to requests out of an initial 30, from the three 1. When we succeeded in completely filling the flow cache, subsequently injected packets occurred for different source ports than in the initial test. If the GC only intercepted a subset of flows to the target IP address, we would expect subsequent injections to appear for the same flows, since most schemes to probabilistically select flows for interception such as hashing the connection 4-tuple would select the same set of flows the second time around.

We determined that the GC uses a separate flow cache for different source IP addresses, and that packets injected from different source IP addresses have distinct TTL side-channels. This finding suggests a load-balanced architecture similar to the GFW, where packets are routed to GC nodes based on source IP address.

We then sent traffic alternating from four measurement IP addresses in an attempt to fill a 16, entry cache. This attempt did not manage to fill the cache, suggesting that the GC not only processed the different source IP addresses with different injection elements, but did so using different flow caches. As stated before, one of the four source IP addresses never received any injected replies.

The staff of GreatFire. To keep our analysis tractable, we examined a sample of the data from March 18th GMT to March 19th GMT, as seen from two of the three most commonly seen backend servers.

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Religion and Morality

For each hour, we selected 30MB of compressed logs for each server. For any IP address that did not result in a definite geolocation using this tool 31 addresses , we looked up the address manually using the iplocation. Note that 8, China, however, accounted for only 18 requests. This is consistent with malicious code injected into China-hosted websites at the border of the Chinese Internet.

The most commonly seen domain is pos. We examined the top 25 domains, and linked each one to Baidu: in each case, the site is either a Baidu property or uses Baidu analytics, advertisements, or static resources. According to Alexa statistics, Baidu itself is the fourth-most visited site globally, the highest ranking China-based site on the global list, 23 and has received an estimated 4. We speculate that Baidu was chosen as an injection target because it is a simple way to target many users.

We believe there is compelling evidence that the Chinese government operates the GC. In recent public statements, China has deflected questions regarding whether they are behind the attack, instead emphasizing that China is often itself a victim of cyber attacks. This co-location across different ISPs strongly suggests a governmental actor.

We are unaware of any public software library for crafting packets that introduces this type of TTL side-channel. Its operation only examines the first data packet of a given connection, which provides a weak censorship mechanism compared to the GFW. The attack on GitHub specifically targeted these repositories, possibly in an attempt to compel GitHub to remove these resources. In the past, China attempted to block Github, but the block was lifted within two days, following significant negative reaction from local programmers. This section describes some policy implications of deploying the Great Cannon, addresses the impact of targeting Baidu traffic specifically, and discusses the Chinese authorities that may be involved in operation of the GC.

Deploying the Great Cannon is a major shift in tactics, and has a highly visible impact. It is likely that this attack, with its potential for political backlash, 28 would require the approval of high-level authorities within the Chinese government. The attack was exceptionally costly to GreatFire, according to their public statements, 33 as well as disruptive to the companies that hosted GreatFire content.

Such a disruption could be both an attempt to block the operations of an undesirable resource, and a signal sent to other organizations of the potential price tag for this kind of activity. Deployment of the GC may also reflect a desire to counter what the Chinese government perceives as US hegemony in cyberspace. The incorporation of Baidu in this attack suggests that the Chinese authorities are willing to pursue domestic stability and security aims at the expense of other goals, including fostering economic growth in the tech sector.

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Conceptual Lacunae and Confusions in the Religion and Morality Debate

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