CVE-2025-55178 is a vulnerability identified in Meta Platforms, Inc's Llama Stack product, specifically affecting versions prior to v0.2.20. The vulnerability is categorized under CWE-78, which corresponds to improper neutralization of special elements used in an OS command, commonly known as OS Command Injection. The root cause lies in the resolve_ast_by_type function, which accepts unverified parameters. This lack of input validation allows an attacker to inject arbitrary OS commands that the system may execute with the privileges of the running application. Since Llama Stack is a software stack developed by Meta, it is likely used in AI or machine learning contexts, potentially integrated into larger systems or services. The vulnerability enables remote code execution (RCE), meaning an attacker can execute arbitrary commands on the affected system remotely without authentication or user interaction, assuming the vulnerable function is exposed or accessible remotely. No known exploits are currently reported in the wild, and no official patches or fixes have been published as of the vulnerability disclosure date (September 24, 2025). The absence of a CVSS score indicates that the severity has not yet been formally assessed, but the nature of the vulnerability suggests a significant risk. The vulnerability's exploitation could lead to full system compromise, data theft, service disruption, or lateral movement within a network. Given that the affected version is listed as '0.0.0', which may be a placeholder or indicate early versions, the vulnerability might primarily affect early adopters or development/test environments rather than widely deployed production systems. However, if Llama Stack is integrated into production environments, the risk is substantial due to the potential for RCE.

For European organizations, the impact of CVE-2025-55178 could be severe, especially for those leveraging Meta's Llama Stack in AI, data processing, or other critical infrastructure. Successful exploitation could lead to unauthorized access to sensitive data, disruption of AI services, or use of compromised systems as pivot points for broader network attacks. Organizations in sectors such as finance, healthcare, telecommunications, and government, which increasingly adopt AI technologies, could face data breaches, operational downtime, and reputational damage. Additionally, the ability to execute arbitrary OS commands remotely increases the risk of malware deployment, ransomware attacks, or espionage activities. The lack of a patch means organizations must rely on immediate mitigations to reduce exposure. The threat is exacerbated if Llama Stack components are exposed to the internet or insufficiently segmented within internal networks. Given the strategic importance of AI technologies in Europe’s digital economy and regulatory frameworks like GDPR, exploitation could also lead to significant compliance and legal consequences.

1. Immediate isolation or removal of vulnerable Llama Stack versions from production environments until a patch is available. 2. Implement strict input validation and sanitization at the application layer, especially for any parameters passed to resolve_ast_by_type or similar functions. 3. Employ network segmentation and firewall rules to restrict access to systems running Llama Stack, limiting exposure to trusted internal networks only. 4. Monitor logs and network traffic for unusual command execution patterns or unexpected process spawning indicative of exploitation attempts. 5. Use application-layer firewalls or runtime application self-protection (RASP) tools to detect and block injection attempts. 6. Engage with Meta Platforms for updates and patches, and subscribe to security advisories for timely remediation. 7. Conduct thorough code reviews and penetration testing focused on command injection vectors in AI stacks and related components. 8. Where feasible, deploy compensating controls such as containerization or sandboxing of Llama Stack processes to limit the impact of potential exploitation.