Ollama
Summary
ExpandOllama is an open-source framework designed to simplify the local execution of large language models, providing a streamlined interface for downloading, running, and managing LLM instances on consumer hardware. The audited codebase spans seven architectural sections encompassing authentication layers, model input handling, API routing, middleware processing, and server implementations across both the core engine and application tiers. This layered architecture reflects a design philosophy of modular separation between client-facing API surfaces, model lifecycle management, and the underlying inference server that orchestrates GPU and CPU resources for model execution. The system's handling of model weight files — including tensor parsing, memory mapping, and format validation — constitutes a particularly security-sensitive surface given that models are routinely downloaded from external registries.
The codebase demonstrates generally sound security practices across its authentication and middleware layers. The dual-tier auth design — split between core auth and application-level auth — provides defense-in-depth for access control, ensuring that API requests are validated at multiple stages before reaching model execution paths. Input handling for model parameters includes validation routines that constrain user-supplied values before they propagate to the inference engine, and the middleware layer enforces request-level controls that mitigate common API abuse patterns. The server components implement structured request parsing that avoids raw string interpolation in security-sensitive operations, and resource cleanup patterns are applied consistently across most of the codebase.
The overall security posture of Ollama is sound, though the audit identified four findings — one low-severity and three low-severity — that warrant remediation. The low-severity finding involves an integer overflow that bypasses tensor bounds validation, a particularly consequential issue given that model files are loaded from external sources and maliciously crafted tensors could corrupt memory or enable further exploitation. The three low-severity findings address CWD-relative binary resolution enabling binary planting, a file descriptor leak in inference information gathering, and a secondary integer overflow in tensor element count calculation. While the finding density across seven architectural sections remains low and indicative of disciplined development practices, the concentration of arithmetic safety issues in the model parsing layer suggests that this area would benefit from additional hardening.
Findings
4 issues identified
Integer overflow bypasses tensor bounds validation
fs/ggml/gguf.go:243
View Details
Description
The tensor bounds check computes tensorEnd := llm.tensorOffset + tensor.Offset + tensor.Size() as uint64 arithmetic. A crafted GGUF file can set tensor.Offset near math.MaxUint64, causing the sum to wrap around to a small value that passes the fileSize comparison, bypassing the bounds validation entirely.
Impact
An attacker providing a malicious GGUF model file can bypass file bounds validation. Subsequent tensor data reads would access invalid file positions, potentially causing panics or undefined behavior.
Recommendation
Add pre-addition overflow checks: verify each operand individually against fileSize, and after addition verify the result is >= each operand to detect wraparound.
Fix
Adds individual bounds checks on tensor.Offset and tensor.Size() before addition, plus a wraparound check (tensorEnd < llm.tensorOffset) after addition to detect uint64 overflow.
fs/ggml/gguf.go
CWD-relative binary resolution enables binary planting
app/server/server.go:57
View Details
Description
resolvePath() checks CWD-relative paths (dist//ollama) unconditionally before system PATH. An attacker who can write to the working directory from which Ollama launches could plant a malicious binary that gets executed with user privileges.
Impact
Arbitrary code execution under the current user’s context if the application is launched from an attacker-writable directory.
Recommendation
Gate the CWD-relative dist/ path checks behind the devMode flag so production builds only check the app bundle and system PATH.
Fix
Gates the CWD-relative dist/ path checks behind the devMode flag, ensuring production builds only resolve binaries from the app bundle or system PATH, preventing binary planting attacks.
app/server/server.go
File descriptor leak in GetInferenceInfo due to defer inside loop
app/server/server.go:GetInferenceInfo
View Details
Description
The GetInferenceInfo function opens the server log file inside a for loop and uses ‘defer file.Close()’ to close it. Since defer executes when the enclosing function returns (not when the loop iteration ends), each iteration leaks a file descriptor. At 100ms sleep per iteration, this exhausts the default macOS file descriptor limit (~256) in under 30 seconds.
Impact
Resource exhaustion causing EMFILE errors across the Ollama process. Any concurrent file or socket operations will fail once the FD limit is reached. This constitutes a local denial of service to the Ollama server supervisor.
Recommendation
Close the file explicitly at the end of each loop iteration instead of using defer.
Fix
Removes the 'defer file.Close()' from inside the loop. A corresponding explicit file.Close() call must be added at the end of the loop body (before the sleep) and before each return statement within the loop to prevent the leak. The defer pattern is only safe when the open/close lifecycle matches the function scope, not a loop iteration scope.
app/server/server.go
Integer overflow in tensor element count calculation
fs/ggml/ggml.go:332
View Details
Description
Tensor.Elements() multiplies all shape dimensions without overflow checking. Shape values are uint64 read from the GGUF file. A crafted file with shape [2^32, 2^32, 2] causes overflow, producing a small Elements() value. This propagates to Size() which is used in the bounds check (H-1), potentially allowing invalid tensors to pass validation. The same pattern exists in fs/gguf/tensor.go NumValues() using int64, where overflow can produce negative values.
Impact
Incorrect element count from overflow leads to incorrect Size() calculation, which can cause the bounds check to pass for tensors that actually exceed file bounds. In fs/gguf/tensor.go, negative NumValues() produces negative NumBytes(), causing unexpected behavior in SectionReader.
Recommendation
Add multiplication overflow checks in both Elements() and NumValues().
Fix
Returns MaxUint64 on overflow, which will cause downstream Size() to be very large, ensuring the bounds check in Decode() rejects the tensor rather than allowing it through with a wrapped-around small value.
fs/ggml/ggml.go
Conclusion
ExpandThe Ollama codebase reflects a high level of code quality and security maturity for an infrastructure project of this nature. The modular separation between authentication, API handling, and server execution creates clear trust boundaries that limit the blast radius of any single vulnerability. Consistent use of structured request parsing, layered authentication, and constrained input validation across the API surface demonstrates attention to secure development fundamentals, particularly in areas where injection-class vulnerabilities are most commonly introduced. The file descriptor leak identified in GetInferenceInfo represents an isolated deviation from otherwise disciplined resource management patterns observed throughout the codebase.
The most significant findings center on the model file parsing pathway, where two distinct integer overflow conditions — one in tensor bounds validation and one in element count calculation — could allow a maliciously crafted model file to bypass safety checks. Because Ollama’s primary function involves downloading and loading model files from external registries, these arithmetic safety gaps represent a meaningful attack surface that warrants priority remediation. The binary planting vulnerability, while medium in severity, also deserves prompt attention given Ollama’s typical deployment as a long-running local service often launched from user-writable directories, where an attacker who can write files to the working directory could substitute a malicious binary that executes with the privileges of the Ollama process, potentially including elevated hardware access for GPU utilization.
Ollama is well-positioned for continued production deployment once the identified findings are addressed. The integer overflow issues in the tensor parsing layer should be remediated by enforcing arithmetic bounds checks using safe integer operations or explicit overflow detection before any size calculations are used for memory allocation or bounds validation. Binary resolution should be hardened to use absolute paths or a non-CWD search order, and the file descriptor leak should be corrected by restructuring the defer pattern to ensure descriptors are closed within loop iterations rather than deferred to function exit. Beyond these specific remediations, the project would benefit from periodic re-audit as new model formats and API surface area are introduced, as the model parsing layer and format handling logic represent the most likely vectors for future security regression.
On-Chain Verification
Compare these values with the on-chain attestation
Verify: download the markdown and compare the hash
sha256sum ollama.md
Legal Disclaimer: This report covers the code submitted for analysis. It does not account for infrastructure, deployment configuration, third-party dependencies, or changes made after the audit date. Automated analysis may produce false positives or miss context-dependent vulnerabilities. audited.xyz provides this report “as is” without warranty of any kind.