TrustTunnel is an open-source VPN protocol with a rule bypass issue in versions prior to 0.9.115. In `tls_listener.rs`, `TlsListener::listen()` peeks 1024 bytes and calls `extract_client_random(...)`. If `parse_tls_plaintext` fails (for example, a fragmented/partial ClientHello split across TCP writes), `extract_client_random` returns `None`. In `rules.rs`, `RulesEngine::evaluate` only evaluates `client_random_prefix` when `client_random` is `Some(...)`. As a result, when extraction fails (`client_random == None`), any rule that relies on `client_random_prefix` matching is skipped and evaluation falls through to later rules. As an important semantics note: `client_random_prefix` is a match condition only. It does not mean "block non-matching prefixes" by itself. A rule with `client_random_prefix = ...` triggers its `action` only when the prefix matches (and the field is available to evaluate). Non-matches (or `None`) simply do not match that rule and continue to fall through. The vulnerability is fixed in version 0.9.115.
This vulnerability carries a MEDIUM severity rating with a CVSS v3.1 score of 5.3, indicating it can be exploited remotely over the network with relatively low complexity without requiring user interaction and does not require pre-existing privileges . The vulnerability impacts limited data confidentiality, for affected systems. Impacting 1 product from adguard organizations running these solutions should prioritize assessment and patching.
Reported in 2026, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.
2026-01-29T22:15:55.047
2026-06-17T10:23:47.003
Analyzed
CVSSv3.1: 5.3 (MEDIUM)
| Type | Vendor | Product | Version/Range | Vulnerable? |
|---|---|---|---|---|
| Application | adguard | trusttunnel | < 0.9.115 | Yes |
SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For adguard's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.