CVE-2026-48100 (GCVE-0-2026-48100)
Vulnerability from cvelistv5 – Published: 2026-09-28 16:43 – Updated: 2026-09-28 17:27
VLAI
EPSS
VEX
Title
Payy: agg_agg trailing message slots are unconstrained and allow forged burn messages
Summary
Payy is an Ethereum L2 zk-rollup for privacy preserving and regulatory compliant transactions. Prior to version 1.3.0, agg_agg forwards the compacted message stream from its inner proofs into a public messages: [Field; 1000] array, but it never checks that the unused tail of the outer array is zero. A registered prover can build a valid agg_final proof for an approved rollup block while inserting an extra burn message after the real messages. RollupV1.verifyRollup() then parses that public input as a normal burn and transfers USDC from the rollup contract to the attacker. This is a severe circuit soundness failure: the proof system accepts a public statement whose messages array is not fully derived from the verified inner proofs. On the current deployment, verifyRollup() is restricted to the existing allowlisted prover, so a fresh public caller cannot submit the invalid proof directly. That gate limits who can reach L1 today; it does not make the circuit statement sound. The issue becomes permissionless under the prover model described in the Payy whitepaper. Section 3.3.2 states: "To join as a prover, the prover is required to submit a small stake", and Section 3.3.1 states that if a prover fails to submit, "other nodes can submit the block proof instead." In that model, an attacker only needs to become a registered prover and use public validator approval data for an already approved block. This issue has been patched in version 1.3.0.
Severity
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-28 17:20 UTC
CWE
- CWE-349 - Acceptance of Extraneous Untrusted Data With Trusted Data
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/polybase/payy/security/advisor… | x_refsource_CONFIRM |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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