Comprehensive Threat Exposure Management Platform
In January 2026, the Linux ecosystem addressed over 1088 vulnerabilities across major distributions including Debian, Red Hat, OpenSUSE, and Ubuntu. More than 585 new vulnerabilities were discovered and patched during this period, spanning critical security issues from information disclosure to privilege escalation and code execution. HiveForce Labs has identified 10 severe vulnerabilities that are actively exploited or have high exploitation potential, requiring immediate attention. These Linux vulnerabilities could enable adversarial tactics such as Execution, Collection, Discovery, and Privilege Escalation. Organizations must upgrade Linux systems to the latest versions with necessary security patches and appropriate security controls to ensure protection against these threats.
In January 2026, the Linux ecosystem addressed over 1088 vulnerabilities across various distributions and products, covering critical issues such as information disclosure, privilege escalation, and code execution. Over 585 new vulnerabilities were discovered and patched across Linux distributions. HiveForce Labs has identified 10 critical vulnerabilities that are either currently being exploited or are highly likely to be exploited in the near future.
These Linux vulnerabilities could facilitate adversarial tactics such as Execution, Collection, Discovery, and Privilege Escalation. Notably, two of these vulnerabilities are under active exploitation in the wild, which requires urgent attention and immediate remediation by organizations running Linux systems.
The most urgent case involves Gogs, a popular self-hosted Git service. A newly identified zero-day flaw, CVE-2025-8110, is actively exploited in the wild and bypasses a previously patched remote code execution issue. The root cause lies in improper symbolic link handling in the PutContents API, which allows authenticated attackers to write files outside repository boundaries. By overwriting sensitive system files, attackers can achieve full code execution on affected systems.
More than 700 Gogs instances are already compromised, and over 1,400 exposed servers still allow open registration by default, creating a broad attack surface for threat actors. Mitigations such as disabling open registration and isolating instances behind VPNs are essential to prevent further exploitation.
At the kernel level, a long-standing vulnerability in the Linux Bluetooth stack remains a serious concern. CVE-2022-3564 is a use-after-free flaw in the l2cap_reassemble_sdu function of the L2CAP protocol. Because Bluetooth protocols in Linux are processed inside the kernel and exposed without authentication, the impact is severe for Linux systems with Bluetooth enabled.
An attacker within Bluetooth range, armed only with the target’s Bluetooth address, can trigger kernel crashes or potentially execute code with full kernel privileges by sending crafted L2CAP packets to vulnerable Linux systems. Compromised or malicious Bluetooth hardware can exploit the same weakness to compromise Linux kernels.
The AI ecosystem is not immune to vulnerabilities. LangChain Core contains a critical serialization injection vulnerability, CVE-2025-68664, known as “LangGrinch.” The issue arises because the dumps() and dumpd() functions fail to properly escape user-controlled dictionaries containing the reserved lc key. During deserialization, especially when secrets_from_env was enabled by default, injected data is interpreted as legitimate LangChain objects.
This allows attackers to extract environment variables holding API keys, database credentials, and other secrets, and in some cases reach code execution. The most common entry point is through LLM-controlled fields such as additional_kwargs or response_metadata, which can be influenced via prompt injection and later serialized during caching, logging, or streaming operations. A parallel flaw in LangChain.js highlights how this insecure pattern crosses language boundaries.
Critical infrastructure faces risk from a memory corruption bug in gpsd, the widely deployed GPS service daemon. CVE-2025-67268 is a heap-based out-of-bounds write in the hnd_129540 function of the NMEA2000 driver. The attack requires no authentication or user interaction and can be launched by injecting malicious packets on a CAN bus or NMEA2000 network. The result ranges from daemon crashes to potential code execution on affected systems. Given gpsd’s role in transportation systems, maritime navigation, and critical infrastructure monitoring, the consequences extend well beyond a single service failure.
Enforce Secure-by-Default Configurations Across All Services
Disable open registration, anonymous access, and unused APIs in platforms like Gogs and similar developer tooling. Treat exposed management interfaces as hostile by default to minimize Linux attack surface.
Harden Trust Boundaries in Application Logic
Validate file paths, symbolic links, array bounds, and serialization inputs rigorously. Assume all user-controlled data, including LLM outputs and metadata fields, is untrusted to prevent exploitation of Linux systems.
Eliminate Unsafe Serialization Patterns
Avoid deserializing user-influenced data into executable objects. In AI frameworks, remove implicit environment secret loading and restrict deserialization to strict, schema-validated formats.
Apply Defense-in-Depth for Memory Safety
Use compiler hardening flags, memory-safe languages where possible, and runtime protections such as ASLR, DEP, and heap sanitization to limit exploit reliability on Linux systems.
Implement Continuous Exposure Monitoring
Actively scan for internet-facing services, misconfigurations, and known vulnerable versions to detect risk before exploitation occurs on Linux infrastructure.
Immediate Risk Containment and Hardening
Reduce exposure without delay by disabling open registration and limiting access strictly to trusted, authenticated users. Place all Gogs instances behind controlled network boundaries such as firewalls or VPNs. Conduct a thorough integrity review of repositories and underlying system files, rotate all credentials, and operate under the assumption of compromise until systems are fully validated.
Secure Serialization and Secret Protection
Assume all serialized and deserialized data is untrusted. Remove or disable unsafe deserialization paths, explicitly disable automatic environment secret loading, and rotate all environment-based credentials. Review all workflows that serialize LLM-influenced fields, including metadata, caching, logging, and streaming, and enforce strict schema validation to prevent object injection and secret exfiltration.
Initial Access
Execution
Defense Evasion
Privilege Escalation
Discovery
Collection
Resource Development
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