``` Filename: 327-pow-over-intro.txt Title: A First Take at PoW Over Introduction Circuits Author: George Kadianakis, Mike Perry, David Goulet, tevador Created: 2 April 2020 Status: Closed 0. Abstract This proposal aims to thwart introduction flooding DoS attacks by introducing a dynamic Proof-Of-Work protocol that occurs over introduction circuits. 1. Motivation So far our attempts at limiting the impact of introduction flooding DoS attacks on onion services has been focused on horizontal scaling with Onionbalance, optimizing the CPU usage of Tor and applying rate limiting. While these measures move the goalpost forward, a core problem with onion service DoS is that building rendezvous circuits is a costly procedure both for the service and for the network. For more information on the limitations of rate-limiting when defending against DDoS, see [REF_TLS_1]. If we ever hope to have truly reachable global onion services, we need to make it harder for attackers to overload the service with introduction requests. This proposal achieves this by allowing onion services to specify an optional dynamic proof-of-work scheme that its clients need to participate in if they want to get served. With the right parameters, this proof-of-work scheme acts as a gatekeeper to block amplification attacks by attackers while letting legitimate clients through. 1.1. Related work For a similar concept, see the three internet drafts that have been proposed for defending against TLS-based DDoS attacks using client puzzles [REF_TLS]. 1.2. Threat model [THREAT_MODEL] 1.2.1. Attacker profiles [ATTACKER_MODEL] This proposal is written to thwart specific attackers. A simple PoW proposal cannot defend against all and every DoS attack on the Internet, but there are adversary models we can defend against. Let's start with some adversary profiles: "The script-kiddie" The script-kiddie has a single computer and pushes it to its limits. Perhaps it also has a VPS and a pwned server. We are talking about an attacker with total access to 10 GHz of CPU and 10 GB of RAM. We consider the total cost for this attacker to be zero $. "The small botnet" The small botnet is a bunch of computers lined up to do an introduction flooding attack. Assuming 500 medium-range computers, we are talking about an attacker with total access to 10 THz of CPU and 10 TB of RAM. We consider the upfront cost for this attacker to be about $400. "The large botnet" The large botnet is a serious operation with many thousands of computers organized to do this attack. Assuming 100k medium-range computers, we are talking about an attacker with total access to 200 THz of CPU and 200 TB of RAM. The upfront cost for this attacker is about $36k. We hope that this proposal can help us defend against the script-kiddie attacker and small botnets. To defend against a large botnet we would need more tools at our disposal (see [FUTURE_DESIGNS]). 1.2.2. User profiles [USER_MODEL] We have attackers and we have users. Here are a few user profiles: "The standard web user" This is a standard laptop/desktop user who is trying to browse the web. They don't know how these defences work and they don't care to configure or tweak them. If the site doesn't load, they are gonna close their browser and be sad at Tor. They run a 2GHz computer with 4GB of RAM. "The motivated user" This is a user that really wants to reach their destination. They don't care about the journey; they just want to get there. They know what's going on; they are willing to make their computer do expensive multi-minute PoW computations to get where they want to be. "The mobile user" This is a motivated user on a mobile phone. Even tho they want to read the news article, they don't have much leeway on stressing their machine to do more computation. We hope that this proposal will allow the motivated user to always connect where they want to connect to, and also give more chances to the other user groups to reach the destination. 1.2.3. The DoS Catch-22 [CATCH22] This proposal is not perfect and it does not cover all the use cases. Still, we think that by covering some use cases and giving reachability to the people who really need it, we will severely demotivate the attackers from continuing the DoS attacks and hence stop the DoS threat all together. Furthermore, by increasing the cost to launch a DoS attack, a big class of DoS attackers will disappear from the map, since the expected ROI will decrease. 2. System Overview 2.1. Tor protocol overview +----------------------------------+ | Onion Service | +-------+ INTRO1 +-----------+ INTRO2 +--------+ | |Client |-------->|Intro Point|------->| PoW |-----------+ | +-------+ +-----------+ |Verifier| | | +--------+ | | | | | | | | | +----------v---------+ | | |Intro Priority Queue| | +---------+--------------------+---+ | | | Rendezvous | | | circuits | | | v v v The proof-of-work scheme specified in this proposal takes place during the introduction phase of the onion service protocol. The system described in this proposal is not meant to be on all the time, and it can be entirely disabled for services that do not experience DoS attacks. When the subsystem is enabled, suggested effort is continuously adjusted and the computational puzzle can be bypassed entirely when the effort reaches zero. In these cases, the proof-of-work subsystem can be dormant but still provide the necessary parameters for clients to voluntarily provide effort in order to get better placement in the priority queue. The protocol involves the following major steps: 1) Service encodes PoW parameters in descriptor [DESC_POW] 2) Client fetches descriptor and computes PoW [CLIENT_POW] 3) Client completes PoW and sends results in INTRO1 cell [INTRO1_POW] 4) Service verifies PoW and queues introduction based on PoW effort [SERVICE_VERIFY] 5) Requests are continuously drained from the queue, highest effort first, subject to multiple constraints on speed [HANDLE_QUEUE] 2.2. Proof-of-work overview 2.2.1. Algorithm overview For our proof-of-work function we will use the Equi-X scheme by tevador [REF_EQUIX]. Equi-X is an asymmetric PoW function based on Equihash<60,3>, using HashX as the underlying layer. It features lightning fast verification speed, and also aims to minimize the asymmetry between CPU and GPU. Furthermore, it's designed for this particular use-case and hence cryptocurrency miners are not incentivized to make optimized ASICs for it. The overall scheme consists of several layers that provide different pieces of this functionality: 1) At the lowest layers, blake2b and siphash are used as hashing and PRNG algorithms that are well suited to common 64-bit CPUs. 2) A custom hash function family, HashX, randomizes its implementation for each new seed value. These functions are tuned to utilize the pipelined integer performance on a modern 64-bit CPU. This layer provides the strongest ASIC resistance, since a hardware reimplementation would need to include a CPU-like pipelined execution unit to keep up. 3) The Equi-X layer itself builds on HashX and adds an algorithmic puzzle that's designed to be strongly asymmetric and to require RAM to solve efficiently. 4) The PoW protocol itself builds on this Equi-X function with a particular construction of the challenge input and particular constraints on the allowed blake2b hash of the solution. This layer provides a linearly adjustable effort that we can verify. 5) Above the level of individual PoW handshakes, the client and service form a closed-loop system that adjusts the effort of future handshakes. The Equi-X scheme provides two functions that will be used in this proposal: - equix_solve(challenge) which solves a puzzle instance, returning a variable number of solutions per invocation depending on the specific challenge value. - equix_verify(challenge, solution) which verifies a puzzle solution quickly. Verification still depends on executing the HashX function, but far fewer times than when searching for a solution. For the purposes of this proposal, all cryptographic algorithms are assumed to produce and consume byte strings, even if internally they operate on some other data type like 64-bit words. This is conventionally little endian order for blake2b, which contrasts with Tor's typical use of big endian. HashX itself is configured with an 8-byte output but its input is a single 64-bit word of undefined byte order, of which only the low 16 bits are used by Equi-X in its solution output. We treat Equi-X solution arrays as byte arrays using their packed little endian 16-bit representation. We tune Equi-X in section [EQUIX_TUNING]. 2.2.2. Dynamic PoW DoS is a dynamic problem where the attacker's capabilities constantly change, and hence we want our proof-of-work system to be dynamic and not stuck with a static difficulty setting. Hence, instead of forcing clients to go below a static target like in Bitcoin to be successful, we ask clients to "bid" using their PoW effort. Effectively, a client gets higher priority the higher effort they put into their proof-of-work. This is similar to how proof-of-stake works but instead of staking coins, you stake work. The benefit here is that legitimate clients who really care about getting access can spend a big amount of effort into their PoW computation, which should guarantee access to the service given reasonable adversary models. See [PARAM_TUNING] for more details about these guarantees and tradeoffs. As a way to improve reachability and UX, the service tries to estimate the effort needed for clients to get access at any given time and places it in the descriptor. See [EFFORT_ESTIMATION] for more details. 2.2.3. PoW effort It's common for proof-of-work systems to define an exponential effort function based on a particular number of leading zero bits or equivalent. For the benefit of our effort estimation system, it's quite useful if we instead have a linear scale. We use the first 32 bits of a hashed version of the Equi-X solution as compared to the full 32-bit range. Conceptually we could define a function: unsigned effort(uint8_t *token) which takes as its argument a hashed solution, interprets it as a bitstring, and returns the quotient of dividing a bitstring of 1s by it. So for example: effort(00000001100010101101) = 11111111111111111111 / 00000001100010101101 or the same in decimal: effort(6317) = 1048575 / 6317 = 165. In practice we can avoid even having to perform this division, performing just one multiply instead to see if a request's claimed effort is supported by the smallness of the resulting 32-bit hash prefix. This assumes we send the desired effort explicitly as part of each PoW solution. We do want to force clients to pick a specific effort before looking for a solution, otherwise a client could opportunistically claim a very large effort any time a lucky hash prefix comes up. Thus the effort is communicated explicitly in our protocol, and it forms part of the concatenated Equi-X challenge. 3. Protocol specification 3.1. Service encodes PoW parameters in descriptor [DESC_POW] This whole protocol starts with the service encoding the PoW parameters in the 'encrypted' (inner) part of the v3 descriptor. As follows: "pow-params" SP type SP seed-b64 SP suggested-effort SP expiration-time NL [At most once] type: The type of PoW system used. We call the one specified here "v1" seed-b64: A random seed that should be used as the input to the PoW hash function. Should be 32 random bytes encoded in base64 without trailing padding. suggested-effort: An unsigned integer specifying an effort value that clients should aim for when contacting the service. Can be zero to mean that PoW is available but not currently suggested for a first connection attempt. See [EFFORT_ESTIMATION] for more details here. expiration-time: A timestamp in "YYYY-MM-DDTHH:MM:SS" format (iso time with no space) after which the above seed expires and is no longer valid as the input for PoW. It's needed so that our replay cache does not grow infinitely. It should be set to RAND_TIME(now+7200, 900) seconds. The service should refresh its seed when expiration-time passes. The service SHOULD keep its previous seed in memory and accept PoWs using it to avoid race-conditions with clients that have an old seed. The service SHOULD avoid generating two consequent seeds that have a common 4 bytes prefix. See [INTRO1_POW] for more info. By RAND_TIME(ts, interval) we mean a time between ts-interval and ts, chosen uniformly at random. 3.2. Client fetches descriptor and computes PoW [CLIENT_POW] If a client receives a descriptor with "pow-params", it should assume that the service is prepared to receive PoW solutions as part of the introduction protocol. The client parses the descriptor and extracts the PoW parameters. It makes sure that the has not expired and if it has, it needs to fetch a new descriptor. The client should then extract the field to configure its PoW 'target' (see [REF_TARGET]). The client SHOULD NOT accept 'target' values that will cause unacceptably long PoW computation. The client uses a "personalization string" P equal to the following nul-terminated ASCII string: "Tor hs intro v1\0". The client looks up `ID`, the current 32-byte blinded public ID (KP_hs_blind_id) for the onion service. To complete the PoW the client follows the following logic: a) Client selects a target effort E, based on and past connection attempt history. b) Client generates a secure random 16-byte nonce N, as the starting point for the solution search. c) Client derives seed C by decoding 'seed-b64'. d) Client calculates S = equix_solve(P || ID || C || N || E) e) Client calculates R = ntohl(blake2b_32(P || ID || C || N || E || S)) f) Client checks if R * E <= UINT32_MAX. f1) If yes, success! The client can submit N, E, the first 4 bytes of C, and S. f2) If no, fail! The client interprets N as a 16-byte little-endian integer, increments it by 1 and goes back to step d). Note that the blake2b hash includes the output length parameter in its initial state vector, so a blake2b_32 is not equivalent to the prefix of a blake2b_512. We calculate the 32-bit blake2b specifically, and interpret it in network byte order as an unsigned integer. At the end of the above procedure, the client should have S as the solution of the Equix-X puzzle with N as the nonce, C as the seed. How quickly this happens depends solely on the target effort E parameter. The algorithm as described is suitable for single-threaded computation. Optionally, a client may choose multiple nonces and attempt several solutions in parallel on separate CPU cores. The specific choice of nonce is entirely up to the client, so parallelization choices like this do not impact the network protocol's interoperability at all. 3.3. Client sends PoW in INTRO1 cell [INTRO1_POW] Now that the client has an answer to the puzzle it's time to encode it into an INTRODUCE1 cell. To do so the client adds an extension to the encrypted portion of the INTRODUCE1 cell by using the EXTENSIONS field (see [PROCESS_INTRO2] section in rend-spec-v3.txt). The encrypted portion of the INTRODUCE1 cell only gets read by the onion service and is ignored by the introduction point. We propose a new EXT_FIELD_TYPE value: [02] -- PROOF_OF_WORK The EXT_FIELD content format is: POW_VERSION [1 byte] POW_NONCE [16 bytes] POW_EFFORT [4 bytes] POW_SEED [4 bytes] POW_SOLUTION [16 bytes] where: POW_VERSION is 1 for the protocol specified in this proposal POW_NONCE is the nonce 'N' from the section above POW_EFFORT is the 32-bit integer effort value, in network byte order POW_SEED is the first 4 bytes of the seed used This will increase the INTRODUCE1 payload size by 43 bytes since the extension type and length is 2 extra bytes, the N_EXTENSIONS field is always present and currently set to 0 and the EXT_FIELD is 41 bytes. According to ticket #33650, INTRODUCE1 cells currently have more than 200 bytes available. 3.4. Service verifies PoW and handles the introduction [SERVICE_VERIFY] When a service receives an INTRODUCE1 with the PROOF_OF_WORK extension, it should check its configuration on whether proof-of-work is enabled on the service. If it's not enabled, the extension SHOULD BE ignored. If enabled, even if the suggested effort is currently zero, the service follows the procedure detailed in this section. If the service requires the PROOF_OF_WORK extension but received an INTRODUCE1 cell without any embedded proof-of-work, the service SHOULD consider this cell as a zero-effort introduction for the purposes of the priority queue (see section [INTRO_QUEUE]). 3.4.1. PoW verification [POW_VERIFY] To verify the client's proof-of-work the service MUST do the following steps: a) Find a valid seed C that starts with POW_SEED. Fail if no such seed exists. b) Fail if N = POW_NONCE is present in the replay cache (see [REPLAY_PROTECTION]) c) Calculate R = ntohl(blake2b_32(P || ID || C || N || E || S)) d) Fail if R * E > UINT32_MAX e) Fail if equix_verify(P || ID || C || N || E, S) != EQUIX_OK f) Put the request in the queue with a priority of E If any of these steps fail the service MUST ignore this introduction request and abort the protocol. In this proposal we call the above steps the "top half" of introduction handling. If all the steps of the "top half" have passed, then the circuit is added to the introduction queue as detailed in section [INTRO_QUEUE]. 3.4.1.1. Replay protection [REPLAY_PROTECTION] The service MUST NOT accept introduction requests with the same (seed, nonce) tuple. For this reason a replay protection mechanism must be employed. The simplest way is to use a simple hash table to check whether a (seed, nonce) tuple has been used before for the active duration of a seed. Depending on how long a seed stays active this might be a viable solution with reasonable memory/time overhead. If there is a worry that we might get too many introductions during the lifetime of a seed, we can use a Bloom filter as our replay cache mechanism. The probabilistic nature of Bloom filters means that sometimes we will flag some connections as replays even if they are not; with this false positive probability increasing as the number of entries increase. However, with the right parameter tuning this probability should be negligible and well handled by clients. {TODO: Design and specify a suitable bloom filter for this purpose.} 3.4.2. The Introduction Queue [INTRO_QUEUE] 3.4.2.1. Adding introductions to the introduction queue [ADD_QUEUE] When PoW is enabled and a verified introduction comes through, the service instead of jumping straight into rendezvous, queues it and prioritizes it based on how much effort was devoted by the client to PoW. This means that introduction requests with high effort should be prioritized over those with low effort. To do so, the service maintains an "introduction priority queue" data structure. Each element in that priority queue is an introduction request, and its priority is the effort put into its PoW: When a verified introduction comes through, the service uses its included effort commitment value to place each request into the right position of the priority_queue: The bigger the effort, the more priority it gets in the queue. If two elements have the same effort, the older one has priority over the newer one. 3.4.2.2. Handling introductions from the introduction queue [HANDLE_QUEUE] The service should handle introductions by pulling from the introduction queue. We call this part of introduction handling the "bottom half" because most of the computation happens in this stage. For a description of how we expect such a system to work in Tor, see [TOR_SCHEDULER] section. 3.4.3. PoW effort estimation [EFFORT_ESTIMATION] 3.4.3.1. High-level description of the effort estimation process The service starts with a default suggested-effort value of 0, which keeps the PoW defenses dormant until we notice signs of overload. The overall process of determining effort can be thought of as a set of multiple coupled feedback loops. Clients perform their own effort adjustments via [CLIENT_TIMEOUT] atop a base effort suggested by the service. That suggestion incorporates the service's control adjustments atop a base effort calculated using a sum of currently-queued client effort. Each feedback loop has an opportunity to cover different time scales. Clients can make adjustments at every single circuit creation request, whereas services are limited by the extra load that frequent updates would place on HSDir nodes. In the combined client/service system these client-side increases are expected to provide the most effective quick response to an emerging DoS attack. After early clients increase the effort using [CLIENT_TIMEOUT], later clients will benefit from the service detecting this increased queued effort and offering a larger suggested_effort. Effort increases and decreases both have an intrinsic cost. Increasing effort will make the service more expensive to contact, and decreasing effort makes new requests likely to become backlogged behind older requests. The steady state condition is preferable to either of these side-effects, but ultimately it's expected that the control loop always oscillates to some degree. 3.4.3.2. Service-side effort estimation Services keep an internal effort estimation which updates on a regular periodic timer in response to measurements made on the queueing behavior in the previous period. These internal effort changes can optionally trigger client-visible suggested_effort changes when the difference is great enough to warrant republishing to the HSDir. This evaluation and update period is referred to as HS_UPDATE_PERIOD. The service side effort estimation takes inspiration from TCP congestion control's additive increase / multiplicative decrease approach, but unlike a typical AIMD this algorithm is fixed-rate and doesn't update immediately in response to events. {TODO: HS_UPDATE_PERIOD is hardcoded to 300 (5 minutes) currently, but it should be configurable in some way. Is it more appropriate to use the service's torrc here or a consensus parameter?} 3.4.3.3. Per-period service state During each update period, the service maintains some state: 1. TOTAL_EFFORT, a sum of all effort values for rendezvous requests that were successfully validated and enqueued. 2. REND_HANDLED, a count of rendezvous requests that were actually launched. Requests that made it to dequeueing but were too old to launch by then are not included. 3. HAD_QUEUE, a flag which is set if at any time in the update period we saw the priority queue filled with more than a minimum amount of work, greater than we would expect to process in approximately 1/4 second using the configured dequeue rate. 4. MAX_TRIMMED_EFFORT, the largest observed single request effort that we discarded during the period. Requests are discarded either due to age (timeout) or during culling events that discard the bottom half of the entire queue when it's too full. 3.4.3.4. Service AIMD conditions At the end of each period, the service may decide to increase effort, decrease effort, or make no changes, based on these accumulated state values: 1. If MAX_TRIMMED_EFFORT > our previous internal suggested_effort, always INCREASE. Requests that follow our latest advice are being dropped. 2. If the HAD_QUEUE flag was set and the queue still contains at least one item with effort >= our previous internal suggested_effort, INCREASE. Even if we haven't yet reached the point of dropping requests, this signal indicates that the our latest suggestion isn't high enough and requests will build up in the queue. 3. If neither condition (1) or (2) are taking place and the queue is below a level we would expect to process in approximately 1/4 second, choose to DECREASE. 4. If none of these conditions match, the suggested effort is unchanged. When we INCREASE, the internal suggested_effort is increased to either its previous value + 1, or (TOTAL_EFFORT / REND_HANDLED), whichever is larger. When we DECREASE, the internal suggested_effort is scaled by 2/3rds. Over time, this will continue to decrease our effort suggestion any time the service is fully processing its request queue. If the queue stays empty, the effort suggestion decreases to zero and clients should no longer submit a proof-of-work solution with their first connection attempt. It's worth noting that the suggested-effort is not a hard limit to the efforts that are accepted by the service, and it's only meant to serve as a guideline for clients to reduce the number of unsuccessful requests that get to the service. The service still adds requests with lower effort than suggested-effort to the priority queue in [ADD_QUEUE]. 3.4.3.5. Updating descriptor with new suggested effort The service descriptors may be updated for multiple reasons including introduction point rotation common to all v3 onion services, the scheduled seed rotations described in [DESC_POW], and updates to the effort suggestion. Even though the internal effort estimate updates on a regular timer, we avoid propagating those changes into the descriptor and the HSDir hosts unless there is a significant change. If the PoW params otherwise match but the seed has changed by less than 15 percent, services SHOULD NOT upload a new descriptor. 4. Client behavior [CLIENT_BEHAVIOR] This proposal introduces a bunch of new ways where a legitimate client can fail to reach the onion service. Furthermore, there is currently no end-to-end way for the onion service to inform the client that the introduction failed. The INTRO_ACK cell is not end-to-end (it's from the introduction point to the client) and hence it does not allow the service to inform the client that the rendezvous is never gonna occur. From the client's perspective there's no way to attribute this failure to the service itself rather than the introduction point, so error accounting is performed separately for each introduction-point. Existing mechanisms will discard an introduction point that's required too many retries. 4.1. Clients handling timeouts [CLIENT_TIMEOUT] Alice can fail to reach the onion service if her introduction request gets trimmed off the priority queue in [HANDLE_QUEUE], or if the service does not get through its priority queue in time and the connection times out. This section presents a heuristic method for the client getting service even in such scenarios. If the rendezvous request times out, the client SHOULD fetch a new descriptor for the service to make sure that it's using the right suggested-effort for the PoW and the right PoW seed. If the fetched descriptor includes a new suggested effort or seed, it should first retry the request with these parameters. {TODO: This is not actually implemented yet, but we should do it. How often should clients at most try to fetch new descriptors? Determined by a consensus parameter? This change will also allow clients to retry effectively in cases where the service has just been reconfigured to enable PoW defenses.} Every time the client retries the connection, it will count these failures per-introduction-point. These counts of previous retries are combined with the service's suggested_effort when calculating the actual effort to spend on any individual request to a service that advertises PoW support, even when the currently advertised suggested_effort is zero. On each retry, the client modifies its solver effort: 1. If the effort is below (CLIENT_POW_EFFORT_DOUBLE_UNTIL = 1000) it will be doubled. 2. Otherwise, multiply the effort by (CLIENT_POW_RETRY_MULTIPLIER = 1.5). 3. Constrain the new effort to be at least (CLIENT_MIN_RETRY_POW_EFFORT = 8) and no greater than (CLIENT_MAX_POW_EFFORT = 10000) {TODO: These hardcoded limits should be replaced by timed limits and/or an unlimited solver with robust cancellation. This is issue tor#40787} 5. Attacker strategies [ATTACK_META] Now that we defined our protocol we need to start tweaking the various knobs. But before we can do that, we first need to understand a few high-level attacker strategies to see what we are fighting against. 5.1.1. Overwhelm PoW verification (aka "Overwhelm top half") [ATTACK_TOP_HALF] A basic attack here is the adversary spamming with bogus INTRO cells so that the service does not have computing capacity to even verify the proof-of-work. This adversary tries to overwhelm the procedure in the [POW_VERIFY] section. That's why we need the PoW algorithm to have a cheap verification time so that this attack is not possible: we tune this PoW parameter in section [POW_TUNING_VERIFICATION]. 5.1.2. Overwhelm rendezvous capacity (aka "Overwhelm bottom half") [ATTACK_BOTTOM_HALF] Given the way the introduction queue works (see [HANDLE_QUEUE]), a very effective strategy for the attacker is to totally overwhelm the queue processing by sending more high-effort introductions than the onion service can handle at any given tick. This adversary tries to overwhelm the procedure in the [HANDLE_QUEUE] section. To do so, the attacker would have to send at least 20 high-effort introduction cells every 100ms, where high-effort is a PoW which is above the estimated level of "the motivated user" (see [USER_MODEL]). An easier attack for the adversary, is the same strategy but with introduction cells that are all above the comfortable level of "the standard user" (see [USER_MODEL]). This would block out all standard users and only allow motivated users to pass. 5.1.3. Hybrid overwhelm strategy [ATTACK_HYBRID] If both the top- and bottom- halves are processed by the same thread, this opens up the possibility for a "hybrid" attack. Given the performance figures for the bottom half (0.31 ms/req.) and the top half (5.5 ms/req.), the attacker can optimally deny service by submitting 91 high-effort requests and 1520 invalid requests per second. This will completely saturate the main loop because: 0.31*(1520+91) ~ 0.5 sec. 5.5*91 ~ 0.5 sec. This attack only has half the bandwidth requirement of [ATTACK_TOP_HALF] and half the compute requirement of [ATTACK_BOTTOM_HALF]. Alternatively, the attacker can adjust the ratio between invalid and high-effort requests depending on their bandwidth and compute capabilities. 5.1.4. Gaming the effort estimation logic [ATTACK_EFFORT] Another way to beat this system is for the attacker to game the effort estimation logic (see [EFFORT_ESTIMATION]). Essentially, there are two attacks that we are trying to avoid: - Attacker sets descriptor suggested-effort to a very high value effectively making it impossible for most clients to produce a PoW token in a reasonable timeframe. - Attacker sets descriptor suggested-effort to a very small value so that most clients aim for a small value while the attacker comfortably launches an [ATTACK_BOTTOM_HALF] using medium effort PoW (see [REF_TEVADOR_1]) 5.1.4. Precomputed PoW attack The attacker may precompute many valid PoW nonces and submit them all at once before the current seed expires, overwhelming the service temporarily even using a single computer. The current scheme gives the attackers 4 hours to launch this attack since each seed lasts 2 hours and the service caches two seeds. An attacker with this attack might be aiming to DoS the service for a limited amount of time, or to cause an [ATTACK_EFFORT] attack. 6. Parameter tuning [POW_TUNING] There are various parameters in this PoW system that need to be tuned: We first start by tuning the time it takes to verify a PoW token. We do this first because it's fundamental to the performance of onion services and can turn into a DoS vector of its own. We will do this tuning in a way that's agnostic to the chosen PoW function. We will then move towards analyzing the client starting difficulty setting for our PoW system. That defines the expected time for clients to succeed in our system, and the expected time for attackers to overwhelm our system. Same as above we will do this in a way that's agnostic to the chosen PoW function. Currently, we have hardcoded the initial client starting difficulty at 8, but this may be too low to ramp up quickly to various on and off attack patterns. A higher initial difficulty may be needed for these, depending on their severity. This section gives us an idea of how large such attacks can be. Finally, using those two pieces we will tune our PoW function and pick the right client starting difficulty setting. At the end of this section we will know the resources that an attacker needs to overwhelm the onion service, the resources that the service needs to verify introduction requests, and the resources that legitimate clients need to get to the onion service. 6.1. PoW verification [POW_TUNING_VERIFICATION] Verifying a PoW token is the first thing that a service does when it receives an INTRODUCE2 cell and it's detailed in section [POW_VERIFY]. This verification happens during the "top half" part of the process. Every millisecond spent verifying PoW adds overhead to the already existing "top half" part of handling an introduction cell. Hence we should be careful to add minimal overhead here so that we don't enable attacks like [ATTACK_TOP_HALF]. During our performance measurements in [TOR_MEASUREMENTS] we learned that the "top half" takes about 0.26 msecs in average, without doing any sort of PoW verification. Using that value we compute the following table, that describes the number of cells we can queue per second (aka times we can perform the "top half" process) for different values of PoW verification time: +---------------------+-----------------------+--------------+ |PoW Verification Time| Total "top half" time | Cells Queued | | | | per second | |---------------------|-----------------------|--------------| | 0 msec | 0.26 msec | 3846 | | 1 msec | 1.26 msec | 793 | | 2 msec | 2.26 msec | 442 | | 3 msec | 3.26 msec | 306 | | 4 msec | 4.26 msec | 234 | | 5 msec | 5.26 msec | 190 | | 6 msec | 6.26 msec | 159 | | 7 msec | 7.26 msec | 137 | | 8 msec | 8.26 msec | 121 | | 9 msec | 9.26 msec | 107 | | 10 msec | 10.26 msec | 97 | +---------------------+-----------------------+--------------+ Here is how you can read the table above: - For a PoW function with a 1ms verification time, an attacker needs to send 793 dummy introduction cells per second to succeed in a [ATTACK_TOP_HALF] attack. - For a PoW function with a 2ms verification time, an attacker needs to send 442 dummy introduction cells per second to succeed in a [ATTACK_TOP_HALF] attack. - For a PoW function with a 10ms verification time, an attacker needs to send 97 dummy introduction cells per second to succeed in a [ATTACK_TOP_HALF] attack. Whether an attacker can succeed at that depends on the attacker's resources, but also on the network's capacity. Our purpose here is to have the smallest PoW verification overhead possible that also allows us to achieve all our other goals. [Note that the table above is simply the result of a naive multiplication and does not take into account all the auxiliary overheads that happen every second like the time to invoke the mainloop, the bottom-half processes, or pretty much anything other than the "top-half" processing. During our measurements the time to handle INTRODUCE2 cells dominates any other action time: There might be events that require a long processing time, but these are pretty infrequent (like uploading a new HS descriptor) and hence over a long time they smooth out. Hence extrapolating the total cells queued per second based on a single "top half" time seems like good enough to get some initial intuition. That said, the values of "Cells queued per second" from the table above, are likely much smaller than displayed above because of all the auxiliary overheads.] 6.2. PoW difficulty analysis [POW_DIFFICULTY_ANALYSIS] The difficulty setting of our PoW basically dictates how difficult it should be to get a success in our PoW system. An attacker who can get many successes per second can pull a successful [ATTACK_BOTTOM_HALF] attack against our system. In classic PoW systems, "success" is defined as getting a hash output below the "target". However, since our system is dynamic, we define "success" as an abstract high-effort computation. Our system is dynamic but we still need a starting difficulty setting that will be used for bootstrapping the system. The client and attacker can still aim higher or lower but for UX purposes and for analysis purposes we do need to define a starting difficulty, to minimize retries by clients. 6.2.1. Analysis based on adversary power In this section we will try to do an analysis of PoW difficulty without using any sort of Tor-related or PoW-related benchmark numbers. We created the table (see [REF_TABLE]) below which shows how much time a legitimate client with a single machine should expect to burn before they get a single success. The x-axis is how many successes we want the attacker to be able to do per second: the more successes we allow the adversary, the more they can overwhelm our introduction queue. The y-axis is how many machines the adversary has in her disposal, ranging from just 5 to 1000. =============================================================== | Expected Time (in seconds) Per Success For One Machine | =========================================================================== | | | Attacker Succeses 1 5 10 20 30 50 | | per second | | | | 5 5 1 0 0 0 0 | | 50 50 10 5 2 1 1 | | 100 100 20 10 5 3 2 | | Attacker 200 200 40 20 10 6 4 | | Boxes 300 300 60 30 15 10 6 | | 400 400 80 40 20 13 8 | | 500 500 100 50 25 16 10 | | 1000 1000 200 100 50 33 20 | | | ============================================================================ Here is how you can read the table above: - If an adversary has a botnet with 1000 boxes, and we want to limit her to 1 success per second, then a legitimate client with a single box should be expected to spend 1000 seconds getting a single success. - If an adversary has a botnet with 1000 boxes, and we want to limit her to 5 successes per second, then a legitimate client with a single box should be expected to spend 200 seconds getting a single success. - If an adversary has a botnet with 500 boxes, and we want to limit her to 5 successes per second, then a legitimate client with a single box should be expected to spend 100 seconds getting a single success. - If an adversary has access to 50 boxes, and we want to limit her to 5 successes per second, then a legitimate client with a single box should be expected to spend 10 seconds getting a single success. - If an adversary has access to 5 boxes, and we want to limit her to 5 successes per second, then a legitimate client with a single box should be expected to spend 1 seconds getting a single success. With the above table we can create some profiles for starting values of our PoW difficulty. 6.2.2. Analysis based on Tor's performance [POW_DIFFICULTY_TOR] To go deeper here, we can use the performance measurements from [TOR_MEASUREMENTS] to get a more specific intuition on the starting difficulty. In particular, we learned that completely handling an introduction cell takes 5.55 msecs in average. Using that value, we can compute the following table, that describes the number of introduction cells we can handle per second for different values of PoW verification: +---------------------+-----------------------+--------------+ |PoW Verification Time| Total time to handle | Cells handled| | | introduction cell | per second | |---------------------|-----------------------|--------------| | 0 msec | 5.55 msec | 180.18 | | 1 msec | 6.55 msec | 152.67 | | 2 msec | 7.55 msec | 132.45 | | 3 msec | 8.55 msec | 116.96 | | 4 msec | 9.55 mesc | 104.71 | | 5 msec | 10.55 msec | 94.79 | | 6 msec | 11.55 msec | 86.58 | | 7 msec | 12.55 msec | 79.68 | | 8 msec | 13.55 msec | 73.80 | | 9 msec | 14.55 msec | 68.73 | | 10 msec | 15.55 msec | 64.31 | +---------------------+-----------------------+--------------+ Here is how you can read the table above: - For a PoW function with a 1ms verification time, an attacker needs to send 152 high-effort introduction cells per second to succeed in a [ATTACK_BOTTOM_HALF] attack. - For a PoW function with a 10ms verification time, an attacker needs to send 64 high-effort introduction cells per second to succeed in a [ATTACK_BOTTOM_HALF] attack. We can use this table to specify a starting difficulty that won't allow our target adversary to succeed in an [ATTACK_BOTTOM_HALF] attack. Of course, when it comes to this table, the same disclaimer as in section [POW_TUNING_VERIFICATION] is valid. That is, the above table is just a theoretical extrapolation and we expect the real values to be much lower since they depend on auxiliary processing overheads, and on the network's capacity. 7. Discussion 7.1. UX This proposal has user facing UX consequences. When the client first attempts a pow, it can note how long iterations of the hash function take, and then use this to determine an estimation of the duration of the PoW. This estimation could be communicated via the control port or other mechanism, such that the browser could display how long the PoW is expected to take on their device. If the device is a mobile platform, and this time estimation is large, it could recommend that the user try from a desktop machine. 7.2. Future work [FUTURE_WORK] 7.2.1. Incremental improvements to this proposal There are various improvements that can be done in this proposal, and while we are trying to keep this v1 version simple, we need to keep the design extensible so that we build more features into it. In particular: - End-to-end introduction ACKs This proposal suffers from various UX issues because there is no end-to-end mechanism for an onion service to inform the client about its introduction request. If we had end-to-end introduction ACKs many of the problems from [CLIENT_BEHAVIOR] would be alleviated. The problem here is that end-to-end ACKs require modifications on the introduction point code and a network update which is a lengthy process. - Multithreading scheduler Our scheduler is pretty limited by the fact that Tor has a single-threaded design. If we improve our multithreading support we could handle a much greater amount of introduction requests per second. 7.2.2. Future designs [FUTURE_DESIGNS] This is just the beginning in DoS defences for Tor and there are various future designs and schemes that we can investigate. Here is a brief summary of these: "More advanced PoW schemes" -- We could use more advanced memory-hard PoW schemes like MTP-argon2 or Itsuku to make it even harder for adversaries to create successful PoWs. Unfortunately these schemes have much bigger proof sizes, and they won't fit in INTRODUCE1 cells. See #31223 for more details. "Third-party anonymous credentials" -- We can use anonymous credentials and a third-party token issuance server on the clearnet to issue tokens based on PoW or CAPTCHA and then use those tokens to get access to the service. See [REF_CREDS] for more details. "PoW + Anonymous Credentials" -- We can make a hybrid of the above ideas where we present a hard puzzle to the user when connecting to the onion service, and if they solve it we then give the user a bunch of anonymous tokens that can be used in the future. This can all happen between the client and the service without a need for a third party. All of the above approaches are much more complicated than this proposal, and hence we want to start easy before we get into more serious projects. 7.3. Environment We love the environment! We are concerned of how PoW schemes can waste energy by doing useless hash iterations. Here is a few reasons we still decided to pursue a PoW approach here: "We are not making things worse" -- DoS attacks are already happening and attackers are already burning energy to carry them out both on the attacker side, on the service side and on the network side. We think that asking legitimate clients to carry out PoW computations is not gonna affect the equation too much, since an attacker right now can very quickly cause the same damage that hundreds of legitimate clients do a whole day. "We hope to make things better" -- The hope is that proposals like this will make the DoS actors go away and hence the PoW system will not be used. As long as DoS is happening there will be a waste of energy, but if we manage to demotivate them with technical means, the network as a whole will less wasteful. Also see [CATCH22] for a similar argument. 8. Acknowledgements Thanks a lot to tevador for the various improvements to the proposal and for helping us understand and tweak the RandomX scheme. Thanks to Solar Designer for the help in understanding the current PoW landscape, the various approaches we could take, and teaching us a few neat tricks. Appendix A. Little-t tor introduction scheduler This section describes how we will implement this proposal in the "tor" software (little-t tor). The following should be read as if tor is an onion service and thus the end point of all inbound data. A.1. The Main Loop [MAIN_LOOP] Tor uses libevent for its mainloop. For network I/O operations, a mainloop event is used to inform tor if it can read on a certain socket, or a connection object in tor. From there, this event will empty the connection input buffer (inbuf) by extracting and processing a cell at a time. The mainloop is single threaded and thus each cell is handled sequentially. Processing an INTRODUCE2 cell at the onion service means a series of operations (in order): 1) Unpack cell from inbuf to local buffer. 2) Decrypt cell (AES operations). 3) Parse cell header and process it depending on its RELAY_COMMAND. 4) INTRODUCE2 cell handling which means building a rendezvous circuit: i) Path selection ii) Launch circuit to first hop. 5) Return to mainloop event which essentially means back to step (1). Tor will read at most 32 cells out of the inbuf per mainloop round. A.2. Requirements for PoW With this proposal, in order to prioritize cells by the amount of PoW work it has done, cells can _not_ be processed sequentially as described above. Thus, we need a way to queue a certain number of cells, prioritize them and then process some cell(s) from the top of the queue (that is, the cells that have done the most PoW effort). We thus require a new cell processing flow that is _not_ compatible with current tor design. The elements are: - Validate PoW and place cells in a priority queue of INTRODUCE2 cells (as described in section [INTRO_QUEUE]). - Defer "bottom half" INTRO2 cell processing for after cells have been queued into the priority queue. A.3. Proposed scheduler [TOR_SCHEDULER] The intuitive way to address the A.2 requirements would be to do this simple and naive approach: 1) Mainloop: Empty inbuf INTRODUCE2 cells into priority queue 2) Process all cells in pqueue 3) Goto (1) However, we are worried that handling all those cells before returning to the mainloop opens possibilities of attack by an adversary since the priority queue is not gonna be kept up to date while we process all those cells. This means that we might spend lots of time dealing with introductions that don't deserve it. See [BOTTOM_HALF_SCHEDULER] for more details. We thus propose to split the INTRODUCE2 handling into two different steps: "top half" and "bottom half" process, as also mentioned in [POW_VERIFY] section above. A.3.1. Top half and bottom half scheduler The top half process is responsible for queuing introductions into the priority queue as follows: a) Unpack cell from inbuf to local buffer. b) Decrypt cell (AES operations). c) Parse INTRODUCE2 cell header and validate PoW. d) Return to mainloop event which essentially means step (1). The top-half basically does all operations of section [MAIN_LOOP] except from (4). An then, the bottom-half process is responsible for handling introductions and doing rendezvous. To achieve this we introduce a new mainloop event to process the priority queue _after_ the top-half event has completed. This new event would do these operations sequentially: a) Pop INTRODUCE2 cell from priority queue. b) Parse and process INTRODUCE2 cell. c) End event and yield back to mainloop. A.3.2. Scheduling the bottom half process [BOTTOM_HALF_SCHEDULER] The question now becomes: when should the "bottom half" event get triggered from the mainloop? We propose that this event is scheduled in when the network I/O event queues at least 1 cell into the priority queue. Then, as long as it has a cell in the queue, it would re-schedule itself for immediate execution meaning at the next mainloop round, it would execute again. The idea is to try to empty the queue as fast as it can in order to provide a fast response time to an introduction request but always leave a chance for more cells to appear between cell processing by yielding back to the mainloop. With this we are aiming to always have the most up-to-date version of the priority queue when we are completing introductions: this way we are prioritizing clients that spent a lot of time and effort completing their PoW. If the size of the queue drops to 0, it stops scheduling itself in order to not create a busy loop. The network I/O event will re-schedule it in time. Notice that the proposed solution will make the service handle 1 single introduction request at every main loop event. However, when we do performance measurements we might learn that it's preferable to bump the number of cells in the future from 1 to N where N <= 32. A.4 Performance measurements This section will detail the performance measurements we've done on tor.git for handling an INTRODUCE2 cell and then a discussion on how much more CPU time we can add (for PoW validation) before it badly degrades our performance. A.4.1 Tor measurements [TOR_MEASUREMENTS] In this section we will derive measurement numbers for the "top half" and "bottom half" parts of handling an introduction cell. These measurements have been done on tor.git at commit 80031db32abebaf4d0a91c01db258fcdbd54a471. We've measured several set of actions of the INTRODUCE2 cell handling process on Intel(R) Xeon(R) CPU E5-2650 v4. Our service was accessed by an array of clients that sent introduction requests for a period of 60 seconds. 1. Full Mainloop Event We start by measuring the full time it takes for a mainloop event to process an inbuf containing INTRODUCE2 cells. The mainloop event processed 2.42 cells per invocation on average during our measurements. Total measurements: 3279 Min: 0.30 msec - 1st Q.: 5.47 msec - Median: 5.91 msec Mean: 13.43 msec - 3rd Q.: 16.20 msec - Max: 257.95 msec 2. INTRODUCE2 cell processing (bottom-half) We also measured how much time the "bottom half" part of the process takes. That's the heavy part of processing an introduction request as seen in step (4) of the [MAIN_LOOP] section: Total measurements: 7931 Min: 0.28 msec - 1st Q.: 5.06 msec - Median: 5.33 msec Mean: 5.29 msec - 3rd Q.: 5.57 msec - Max: 14.64 msec 3. Connection data read (top half) Now that we have the above pieces, we can use them to measure just the "top half" part of the procedure. That's when bytes are taken from the connection inbound buffer and parsed into an INTRODUCE2 cell where basic validation is done. There is an average of 2.42 INTRODUCE2 cells per mainloop event and so we divide that by the full mainloop event mean time to get the time for one cell. From that we subtract the "bottom half" mean time to get how much the "top half" takes: => 13.43 / (7931 / 3279) = 5.55 => 5.55 - 5.29 = 0.26 Mean: 0.26 msec To summarize, during our measurements the average number of INTRODUCE2 cells a mainloop event processed is ~2.42 cells (7931 cells for 3279 mainloop invocations). This means that, taking the mean of mainloop event times, it takes ~5.55msec (13.43/2.42) to completely process an INTRODUCE2 cell. Then if we look deeper we see that the "top half" of INTRODUCE2 cell processing takes 0.26 msec in average, whereas the "bottom half" takes around 5.33 msec. The heavyness of the "bottom half" is to be expected since that's where 95% of the total work takes place: in particular the rendezvous path selection and circuit launch. A.2. References [REF_EQUIX]: https://github.com/tevador/equix https://github.com/tevador/equix/blob/master/devlog.md [REF_TABLE]: The table is based on the script below plus some manual editing for readability: https://gist.github.com/asn-d6/99a936b0467b0cef88a677baaf0bbd04 [REF_BOTNET]: https://media.kasperskycontenthub.com/wp-content/uploads/sites/43/2009/07/01121538/ynam_botnets_0907_en.pdf [REF_CREDS]: https://lists.torproject.org/pipermail/tor-dev/2020-March/014198.html [REF_TARGET]: https://en.bitcoin.it/wiki/Target [REF_TLS]: https://www.ietf.org/archive/id/draft-nygren-tls-client-puzzles-02.txt https://datatracker.ietf.org/doc/html/draft-nir-tls-puzzles-00.html https://tools.ietf.org/html/draft-ietf-ipsecme-ddos-protection-10 [REF_TLS_1]: https://www.ietf.org/archive/id/draft-nygren-tls-client-puzzles-02.txt [REF_TEVADOR_1]: https://lists.torproject.org/pipermail/tor-dev/2020-May/014268.html [REF_TEVADOR_2]: https://lists.torproject.org/pipermail/tor-dev/2020-June/014358.html [REF_TEVADOR_SIM]: https://github.com/mikeperry-tor/scratchpad/blob/master/tor-pow/effort_sim.py#L57 ```