### Summary Xgrammar includes a cache for compiled grammars to increase performance with repeated use of the same grammar. This cache is held in memory. Since the cache is unbounded, a system making use of xgrammar can be abused to fill up a host's memory and case a denial of service. For example, sending many small requests to an LLM inference server with unique JSON schemas would eventually cause this denial of service to occur. ### Details The fix is to add a limit to the cache size. This was done in https://github.com/mlc-ai/xgrammar/pull/243 An example of making use of the new cache size limit can be found in vLLM here: https://github.com/vllm-project/vllm/pull/16283 ### Impact Any system making use of Xgrammar and taking requests as input from potentially untrusted parties would be vulnerable to this denial of service issue.
### Summary Xgrammar includes a cache for compiled grammars to increase performance with repeated use of the same grammar. This cache is held in memory. Since the cache is unbounded, a system making use of xgrammar can be abused to fill up a host's memory and case a denial of service. For example, sending many small requests to an LLM inference server with unique JSON schemas would eventually cause this denial of service to occur. ### Details The fix is to add a limit to the cache size. This was done in https://github.com/mlc-ai/xgrammar/pull/243 An example of making use of the new cache size limit can be found in vLLM here: https://github.com/vllm-project/vllm/pull/16283 ### Impact Any system making use of Xgrammar and taking requests as input from potentially untrusted parties would be vulnerable to this denial of service issue.
### Summary Xgrammar includes a cache for compiled grammars to increase performance with repeated use of the same grammar. This cache is held in memory. Since the cache is unbounded, a system making use of xgrammar can be abused to fill up a host's memory and case a denial of service. For example, sending many small requests to an LLM inference server with unique JSON schemas would eventually cause this denial of service to occur. ### Details The fix is to add a limit to the cache size. This was done in https://github.com/mlc-ai/xgrammar/pull/243 An example of making use of the new cache size limit can be found in vLLM here: https://github.com/vllm-project/vllm/pull/16283 ### Impact Any system making use of Xgrammar and taking requests as input from potentially untrusted parties would be vulnerable to this denial of service issue.
### Summary Xgrammar includes a cache for compiled grammars to increase performance with repeated use of the same grammar. This cache is held in memory. Since the cache is unbounded, a system making use of xgrammar can be abused to fill up a host's memory and case a denial of service. For example, sending many small requests to an LLM inference server with unique JSON schemas would eventually cause this denial of service to occur. ### Details The fix is to add a limit to the cache size. This was done in https://github.com/mlc-ai/xgrammar/pull/243 An example of making use of the new cache size limit can be found in vLLM here: https://github.com/vllm-project/vllm/pull/16283 ### Impact Any system making use of Xgrammar and taking requests as input from potentially untrusted parties would be vulnerable to this denial of service issue.
Update xgrammar to 0.1.18 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanxgrammar Vulnerable to Denial of Service (DoS) by abusing unbounded cache in memory affects xgrammar (pip). Severity is medium. ### Summary Xgrammar includes a cache for compiled grammars to increase performance with repeated use of the same grammar. This cache is held in memory. Since the cache is unbounded, a system making use of xgrammar can be abused to fill up a host's memory and case a denial of service. For example, sending many small requests to an LLM inference server with unique JSON schemas would eventually cause this denial of service to occur. ### Details The fix is to add a limit to the cache size. This was done in https://github.com/mlc-ai/xgrammar/pull/243 An example of making use of the new cache size limit can be found in vLLM here: https://github.com/vllm-project/vllm/pull/16283 ### Impact Any system making use of Xgrammar and taking requests as input from potentially untrusted parties would be vulnerable to this denial of service issue.
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| xgrammarpip | <0.1.18 | 0.1.18 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL GuardUpdate xgrammar to 0.1.18 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanxgrammar Vulnerable to Denial of Service (DoS) by abusing unbounded cache in memory affects xgrammar (pip). Severity is medium. ### Summary Xgrammar includes a cache for compiled grammars to increase performance with repeated use of the same grammar. This cache is held in memory. Since the cache is unbounded, a system making use of xgrammar can be abused to fill up a host's memory and case a denial of service. For example, sending many small requests to an LLM inference server with unique JSON schemas would eventually cause this denial of service to occur. ### Details The fix is to add a limit to the cache size. This was done in https://github.com/mlc-ai/xgrammar/pull/243 An example of making use of the new cache size limit can be found in vLLM here: https://github.com/vllm-project/vllm/pull/16283 ### Impact Any system making use of Xgrammar and taking requests as input from potentially untrusted parties would be vulnerable to this denial of service issue.
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| xgrammarpip | <0.1.18 | 0.1.18 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL Guard