Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain
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AccessIdentify exposed LiteLLM proxy endpoint
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DeliverySend POST request with SQLi payload in Authorization header
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ExploitTrigger error handler with malicious query
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ExecutionExecute arbitrary SQL against proxy database
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PersistExtract LLM API credentials
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ImpactUse stolen credentials to access LLM services directly
Vulnerability Assessment
| Weakness |
CWE
OWASP Top 10:2025
A05:2025 - Injection
ASVS 5.0
V1 - Encoding and Sanitization Parameterized queries, prepared statements
MITRE ATT&CK
2 techniques across 2 tactics: Initial Access, Persistence The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up. |
| Exploitation Conditions | No special conditions - remote unauthenticated exploitation against default configurations of LiteLLM proxy server versions 1.81.16-1.83.6. Attack requires only network access to any LiteLLM API endpoint (commonly exposed on TCP/8000 or via reverse proxy). Exploitation specifically requires triggering the error-handling code path during API key verification, achieved by sending requests with malformed or invalid Authorization headers that contain SQL injection payloads. The vendor workaround (disable_error_logs: true) explicitly blocks this attack path, limiting exploitation to installations running affected versions without this configuration change. Attack complexity is low (AC:L) as no race conditions, timing dependencies, or user interaction is required. |
| Impact | CVSS 4.0 score of 9.3 reflects critical severity with maximum exploitability (AV:N/AC:L/PR:N/UI:N) - remote unauthenticated attackers can exploit this without special conditions. However, real-world risk assessment reveals nuanced factors: the vulnerability requires reaching the error-handling path rather than normal authentication flow, which may limit exploitation to specific request patterns. No CISA KEV listing indicates no confirmed widespread exploitation at time of analysis. EPSS data not provided, but SQL injection vulnerabilities typically have moderate-to-high exploitation probability given mature tooling and attacker familiarity. High technical impact (VC:H/VI:H) is confirmed - attackers gain read/write access to credential databases. The attack is automatable once the specific payload pattern is identified. Scope is unchanged (SC:N/SI:N/SA:N), meaning impact is confined to the vulnerable LiteLLM proxy. Priority assessment: HIGH for organizations running affected versions with internet-facing LiteLLM proxies managing production LLM credentials; MEDIUM for internally-facing deployments with network segmentation; all should patch given the trivial exploit path and credential exposure risk. |
| Exploit ScenarioDerived | An unauthenticated attacker crafts a malicious Authorization header containing SQL injection payloads (e.g., ' OR 1=1-- or UNION-based queries) and sends it to the LiteLLM proxy's /chat/completions endpoint or any other LLM API route. The proxy's error-handling path during API key verification concatenates this header value into a SQL query, allowing the attacker to execute arbitrary database commands. The attacker extracts stored LLM API keys (OpenAI, Anthropic, etc.) from the database, then uses these credentials to directly access LLM services, bypassing the proxy's usage controls and potentially incurring significant costs or accessing sensitive data processed by the LLMs. Publicly available SQL injection tools and techniques make exploitation straightforward once the vulnerable endpoint pattern is identified. |
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Alert meAnalysisAIv1 – May 08, 2026
SQL injection in LiteLLM proxy server versions 1.81.16 through 1.83.6 allows unauthenticated remote attackers to read and modify database contents, gaining unauthorized access to managed LLM API credentials. The vulnerability is exploitable via crafted Authorization headers sent to any LLM API route (e.g., POST /chat/completions), triggering the injection through the proxy's error-handling path. Vendor-released patch available in version 1.83.7. No active exploitation confirmed (not in CISA KEV), but the attack vector is simple (CVSS 4.0: AV:N/AC:L/PR:N) and SQL injection POCs are widely known. Discovered by Tencent YunDing Security Lab.
RemediationAI
Upgrade to LiteLLM version 1.83.7 or later, available at https://github.com/BerriAI/litellm/releases/tag/v1.83.7-stable (patched release confirmed by vendor). The fix implements parameterized SQL queries for API key verification, eliminating the injection vector. For environments where immediate upgrade is not feasible, apply the vendor-recommended workaround: set disable_error_logs: true under general_settings in the LiteLLM configuration, which removes the error-handling code path through which unauthenticated input reaches the vulnerable query. Note this workaround may reduce operational visibility into authentication failures and should be considered temporary. After upgrading, rotate all LLM API credentials managed by the proxy, as attackers who previously exploited this vulnerability may have exfiltrated credential data. Docker users should verify image signatures using cosign per vendor instructions to ensure authenticity of the patched release.
Affected VersionsENISA
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 1 package in pypi
Checking this CVE against your own stacks and servers is part of the Basic plan and up.
Technical ContextAI · show details
LiteLLM is a proxy server (AI Gateway) that translates requests to various LLM APIs into OpenAI-compatible format. The vulnerability stems from CWE-89 (SQL Injection) where user-supplied API key values from the Authorization header are concatenated directly into SQL query text during API key verification, rather than being passed as parameterized values. The affected component is the proxy's authentication layer, specifically within the error-handling code path. CPE identifier cpe:2.3:a:berriai:litellm indicates this affects the Python package distributed via PyPI. The flaw violates secure coding practices for database interactions, allowing attackers to break out of the intended query logic and execute arbitrary SQL commands against the proxy's backend database, which stores sensitive credential data for managed LLM services.
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Severity by source
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
Primary rating from Vendor (GitHub_M).
CVSS VectorVendor: GitHub_M
Lifecycle Timeline
4DescriptionCVE.org
LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. From version 1.81.16 to before version 1.83.7, a database query used during proxy API key checks mixed the caller-supplied key value into the query text instead of passing it as a separate parameter. An unauthenticated attacker could send a specially crafted Authorization header to any LLM API route (for example POST /chat/completions) and reach this query through the proxy's error-handling path. An attacker could read data from the proxy's database and may be able to modify it, leading to unauthorised access to the proxy and the credentials it manages. This issue has been patched in version 1.83.7.
Articles & Coverage 3
Executive ViewAI
LiteLLM proxy server versions 1.81.16-1.83.6 contain a critical SQL injection vulnerability accessible without authentication that allows attackers to read and modify database contents, including stored LLM API credentials. Organizations running affected versions face immediate risk of credential theft and lateral movement to cloud LLM services. This vulnerability requires urgent patching due to its simplicity of exploitation and the sensitivity of data exposed.
Apply vendor patch to version 1.83.7 or later. If immediate patching is not possible: implement network-level access controls to restrict LiteLLM proxy endpoints to trusted internal networks only; monitor all requests to /chat/completions and similar routes for malformed Authorization headers; consider running affected instances in isolated network segments pending upgrade.
Threat Intelligence Overview
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
SQL injection exploits the way applications construct database queries by mixing user input directly into SQL statements.
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EUVD-2026-28503
GHSA-r75f-5x8p-qvmc
NVD
CVE.org
CISA KEV
EPSS
Vulnrichment
VulDB
Exploit-DB