GHSA-438q-jx8f-cccv CVE-2026-44500
Zebra Vulnerable to Allocation Amplification in Inbound Network Deserializers
- Severity
- 5.3 / 10
- Medium · CVSS 3.1
- Exploit chance (EPSS)
- 0.36%
- Chance of exploitation in the next 30 days. Higher than 30.09% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-770 Allocation of Resources Without Limits or Throttling
- Fix
- Available
- 3 packages have a fixed version
Description
CVE-2026-44500: Allocation Amplification in Inbound Network Deserializers
Summary
Several inbound deserialization paths in Zebra allocated buffers sized against generic transport or block-size ceilings before the tighter protocol or consensus limits were enforced. An unauthenticated or post-handshake peer could therefore force the node to preallocate and parse for orders of magnitude more data than the protocol intended, across headers messages, equihash solutions in block headers, Sapling spend vectors in V5/V4 transactions, and coinbase script bytes in blocks.
Severity
Moderate - This is a Denial-of-Service Vulnerability that could allow a malicious peer to amplify per-message memory and parse cost on Zebra nodes, with effects amplified by multi-peer fan-in.
Each individual case is bounded by the 2 MiB transport ceiling or the block-size cap, so no single message causes unbounded allocation, but the cumulative gap between intended and actual limits is significant.
Affected Versions
All Zebra versions prior to 4.4.0.
Description
Zebra's network codec uses TrustedPreallocate and generic Vec deserialization to bound inbound message parsing. In several places the bound used at the deserializer was the generic transport or block-size ceiling rather than the tighter protocol or consensus rule that applies to the field, so allocation happened first and the real limit was only enforced afterwards. Four such cases were identified:
headersmessage receive cap.read_headers()deserialized theCountedHeadervector via the genericTrustedPreallocatepath, which allowed up to ~1,409 entries per message. The protocol ceilingMAX_FIND_BLOCK_HEADERS_RESULTS = 160was only used on the send side, giving an ~8.8x preallocation gap on receive. Reachable before the version handshake completes since the codec is installed on raw bytes.- Equihash solution length.
Solution::zcash_deserializedecoded the solution as a genericVec<u8>and only checked the exact consensus size (1344 bytes mainnet/testnet, 36 bytes regtest) afterwards inSolution::from_bytes. A single fixed-size header field could be inflated to nearly the full block-size ceiling before rejection. - Sapling spend vectors in coinbase transactions. V5
spend_prefixesand V4shielded_spendswere allocated generically with block-size-derived ceilings (~5,681 / ~5,208 entries) before the consensus rule that coinbase transactions have zero Sapling spends was enforced in the verifier. - Coinbase script bytes.
Input::zcash_deserialize()read the coinbase script as a genericVec<u8>up to the message-size cap before enforcing the consensus rule that coinbase scripts are between 2 and 100 bytes.
An attacker could exploit this by:
- Opening an inbound TCP connection (and, for the latter three cases, completing the version handshake).
- Sending one of: a
headersmessage with a CompactSize count up to ~1,409, ablockwhose header carries an inflated equihash CompactSize, atxdeclaring a coinbase input with a largenSpendsSapling, or ablockwith a coinbase input whose script length is near the message-size ceiling. - The deserializer allocates against the loose ceiling, parses, and only then rejects.
Impact
Denial of Service
- Attack Vector: Network.
- Effect: Amplified per-message allocation and parse cost on inbound peer messages, stackable across concurrent connections. The concrete effect will be influenced by how much memory Zebra has available.
- Scope: Any affected Zebra node.
Fixed Versions
This issue is fixed in Zebra 4.4.0.
Mitigation
Users should upgrade to Zebra 4.4.0 or later immediately.
There are no known workarounds for this issue. Immediate upgrade is the only way to remove the amplified allocation surface on inbound peer messages.
Credits
Zebra thanks @Zk-nd3r for finding and reporting the issues.
Affected versions
crates.io
| Package | Vulnerable | Fixed in |
|---|---|---|
| zebra-chain | < 7.0.0 |
7.0.0 |
| zebra-network | < 6.0.0 |
6.0.0 |
| zebrad | < 4.4.0 |
4.4.0 |
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
zebra-chain crates.io → 7.0.0
- cargo
cargo update -p zebra-chain --precise 7.0.0
zebra-network crates.io → 6.0.0
- cargo
cargo update -p zebra-network --precise 6.0.0
zebrad crates.io → 4.4.0
- cargo
cargo update -p zebrad --precise 4.4.0
Severity breakdown
CVSS 3.1 5.3
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- None
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- None
- Integrity
- None
- Availability
- Low
Weakness
CWE-770: Allocation of Resources Without Limits or Throttling
The product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated.
How to prevent it
- Requirements: Clearly specify the minimum and maximum expectations for capabilities, and dictate which behaviors are acceptable when resource allocation reaches limits.
- Architecture and Design: Limit the amount of resources that are accessible to unprivileged users. Set per-user limits for resources. Allow the system administrator to define these limits. Be careful to avoid CWE-410.
- Architecture and Design: Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place, and it will help the administrator to identify who is committing the abuse. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold.
- Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
- Architecture and Design: For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
- Architecture and Design: Mitigation of resource exhaustion attacks requires that the target system either: recognizes the attack and denies that user further access for a given amount of time, typically by using increasing time delays uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed. The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question. The second solution can be difficult to effectively institute -- and even when properly done, it does not provide a full solution. It simply requires more resources on the part of the attacker.
- Architecture and Design: Ensure that protocols have specific limits of scale placed on them.
- Architecture and Design, Implementation: If the program must fail, ensure that it fails gracefully (fails closed). There may be a temptation to simply let the program fail poorly in cases such as low memory conditions, but an attacker may be able to assert control before the software has fully exited. Alternately, an uncontrolled failure could cause cascading problems with other downstream components; for example, the program could send a signal to a downstream process so the process immediately knows that a problem has occurred and has a better chance of recovery. Ensure that all failures in resource allocation place the system into a safe posture.
- Operation, Architecture and Design: Use quotas or other resource-limiting settings provided by the operating system or environment. For example, when managing system resources in POSIX, setrlimit() can be used to set limits for certain types of resources, and getrlimit() can determine how many resources are available. However, these functions are not available on all operating systems. When the current levels get close to the maximum that is defined for the application (see CWE-770), then limit the allocation of further resources to privileged users; alternately, begin releasing resources for less-privileged users. While this mitigation may protect the system from attack, it will not necessarily stop attackers from adversely impacting other users. Ensure that the application performs the appropriate error checks and error handling in case resources become unavailable (CWE-703).
References
Advisories
Packages
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| GHSA-ww9q-8r59-xv46 Zebra: Missing copy constraint in halo2_gadgets variable-base scalar multiplication allows under-constrained base, breaking Orchard Action circuit soundness | Critical | Also affects zebrad |
| GHSA-63wg-wjjj-7cp8 Zebra Address Book Aborted by IPv4-Mapped Mempool Misbehavior Update | High | Also affects zebrad |
| GHSA-65jj-fmw8-468q zebrad has unbounded memory leak in mempool download pipeline via timeout path cancel_handles retention | Medium | Also affects zebrad |
| GHSA-2gf8-q9rr-jq3h zebrad has persistent on-disk corruption of Sapling/Orchard subtree roots after chain fork via pop_tip | Medium | Also affects zebrad |
| GHSA-hhm7-qrv5-h4r6 Zebra: Repeated Non-Finalized Shielded Transaction Aborts Zebra Before Duplicate-Nullifier Rejection | Medium | Also affects zebrad |
| GHSA-w834-cf6p-9m9w Zebra: Finalized address balance credit-first overflow on consensus-valid blocks | Medium | Also affects zebrad |
| GHSA-gvjc-3w7c-92jx Zebra has sync restart poisoning from single unauthenticated peer via above-lookahead block | Medium | Also affects zebrad |
| GHSA-gf9r-m956-97qx zebrad has consensus divergence via P2SH sigop undercount in pure-Rust disabled-opcode parser | Critical | Also affects zebrad |
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