Summary
NVIDIA Isaac Lab contains an eval injection vulnerability that may allow attacker-controlled input to be evaluated as executable code. A successful exploit could result in code execution, information disclosure, data tampering, or denial of service when an exposed evaluation path processes crafted input.
NVIDIA Isaac Lab contains an eval injection vulnerability that may allow attacker-controlled input to be evaluated as executable code. A successful exploit could result in code execution, information disclosure, data tampering, or denial of service when an exposed evaluation path processes crafted input.
Affected context
The vulnerability may occur in NVIDIA Isaac Lab components that evaluate dynamically constructed expressions or other attacker-influenced input. Risk depends on whether untrusted data can reach the evaluation mechanism and whether the affected process has access to sensitive files, services, or simulation resources.
The vulnerability may occur in NVIDIA Isaac Lab components that evaluate dynamically constructed expressions or other attacker-influenced input. Risk depends on whether untrusted data can reach the evaluation mechanism and whether the affected process has access to sensitive files, services, or simulation resources.
Exploit preconditions
- Attack surface: An application, service, workflow, or interface that passes external input to an evaluation function
- Required access: The ability to submit or influence data consumed by the affected evaluation path
- Trigger point: Processing of attacker-controlled input by the dynamic evaluation logic
- Why exploitation is possible: Input is treated as executable expression content rather than as inert data, allowing unintended operations within the privileges of the hosting process
- Attack surface: An application, service, workflow, or interface that passes external input to an evaluation function
- Required access: The ability to submit or influence data consumed by the affected evaluation path
- Trigger point: Processing of attacker-controlled input by the dynamic evaluation logic
- Why exploitation is possible: Input is treated as executable expression content rather than as inert data, allowing unintended operations within the privileges of the hosting process
Why this happens
The underlying issue is eval injection, mapped to CWE-95: Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection'). The insecure pattern occurs when an application constructs or evaluates code-like expressions using data that has not been strictly constrained to an expected set of values. Because evaluation interprets input as executable logic, specially crafted content may alter program behavior or invoke unintended operations.
The underlying issue is eval injection, mapped to CWE-95: Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection'). The insecure pattern occurs when an application constructs or evaluates code-like expressions using data that has not been strictly constrained to an expected set of values. Because evaluation interprets input as executable logic, specially crafted content may alter program behavior or invoke unintended operations.
Potential impact
- An attacker may execute code in the context of the affected process
- Sensitive information could be disclosed
- Application or simulation data may be altered or corrupted
- The service could become unavailable through denial-of-service conditions
- Compromised processes could be used to access connected resources, depending on permissions
- An attacker may execute code in the context of the affected process
- Sensitive information could be disclosed
- Application or simulation data may be altered or corrupted
- The service could become unavailable through denial-of-service conditions
- Compromised processes could be used to access connected resources, depending on permissions
General mitigation guidance
Mitigation generally focuses on removing dynamic evaluation of untrusted input and reducing the trust placed in externally supplied expressions.
Common defensive patterns include:
- Replacing general-purpose evaluation with explicit operations, lookup tables, or a constrained expression language
- Enforcing allowlisted values, types, operators, and resource references before processing input
- Validating input against a strict schema and rejecting unexpected syntax or semantics
- Keeping evaluation, where unavoidable, isolated with strong execution and resource boundaries
- Running affected services with least-privilege identities and limited filesystem, network, and device access
- Separating user-controlled data from executable configuration and policy logic
Mitigation generally focuses on removing dynamic evaluation of untrusted input and reducing the trust placed in externally supplied expressions.
Common defensive patterns include:
- Replacing general-purpose evaluation with explicit operations, lookup tables, or a constrained expression language
- Enforcing allowlisted values, types, operators, and resource references before processing input
- Validating input against a strict schema and rejecting unexpected syntax or semantics
- Keeping evaluation, where unavoidable, isolated with strong execution and resource boundaries
- Running affected services with least-privilege identities and limited filesystem, network, and device access
- Separating user-controlled data from executable configuration and policy logic
How teams detect issues like this
Detection approaches may include:
- Static analysis rules identifying calls to
eval, dynamic expression interpreters, or equivalent APIs - Code review checks for string concatenation or formatting used to construct evaluated expressions
- Searches for user-controlled, file-based, or network-derived values flowing into evaluation functions
- Runtime policy enforcement that blocks unexpected expression syntax or disallowed operations
- Logging and monitoring of evaluation failures, anomalous expressions, and unusual process behavior
Automated code review and software composition tools can help identify dynamic evaluation patterns during development.
Detection approaches may include:
- Static analysis rules identifying calls to
eval, dynamic expression interpreters, or equivalent APIs - Code review checks for string concatenation or formatting used to construct evaluated expressions
- Searches for user-controlled, file-based, or network-derived values flowing into evaluation functions
- Runtime policy enforcement that blocks unexpected expression syntax or disallowed operations
- Logging and monitoring of evaluation failures, anomalous expressions, and unusual process behavior
Automated code review and software composition tools can help identify dynamic evaluation patterns during development.
Sources
| Source | Link |
|---|---|
| github. | github.com/ |
| nvd. | nvd.nist.gov/ |
| cve. | cve.org/ |
