CVE-2026-77956 (GCVE-0-2026-77956)
Vulnerability from cvelistv5 – Published: 2026-08-31 00:56 – Updated: 2026-08-31 16:01
VLAI
EPSS
VEX
Title
EEx template evaluation of prompt content in AshAi enables remote code execution
Summary
Improper Control of Generation of Code (Code Injection) vulnerability in ash-project ash_ai allows a remote, unauthenticated client to execute arbitrary Elixir code.
AshAi.Actions.Prompt evaluates prompt content through EEx.eval_string/2. The documented prompt: fn input, context -> ... end form lets the prompt content be built from action arguments, so when a prompt action's text incorporates request data, that attacker-controlled text is compiled and run as an EEx template (Elixir source). Content such as <%= System.cmd(...) %> therefore executes on the server before any model request is made, requiring no authentication beyond reaching a prompt action. The fix stops evaluating function-supplied prompt content as EEx; only statically configured templates are evaluated.
This issue affects ash_ai: from 0.1.0 before 1.0.0.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-31 16:01 UTC
CWE
- CWE-94 - Improper Control of Generation of Code ('Code Injection')
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/ash-project/ash_ai/security/ad… | vendor-advisoryrelated |
| https://cna.erlef.org/cves/CVE-2026-77956.html | related |
| https://osv.dev/vulnerability/EEF-CVE-2026-77956 | related |
| https://github.com/ash-project/ash_ai/commit/e994… | patch |
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| ash-project | ash_ai |
Affected:
0.1.0 , < 1.0.0
(semver)
cpe:2.3:a:ash-project:ash_ai:*:*:*:*:*:*:*:* |
||
| ash-project | ash_ai |
Affected:
4aab131d40a0bd5a8cf0b3c4eaaa59d49565f3d1 , < e9948254b5659c1143b73dc2f59f457931e64514
(git)
cpe:2.3:a:ash-project:ash_ai:*:*:*:*:*:*:*:* |
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"value": "\u003cp\u003eImproper Control of Generation of Code (Code Injection) vulnerability in ash-project ash_ai allows a remote, unauthenticated client to execute arbitrary Elixir code.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eAshAi.Actions.Prompt\u003c/code\u003e evaluates prompt content through \u003ccode\u003eEEx.eval_string/2\u003c/code\u003e. The documented \u003ccode\u003eprompt: fn input, context -\u0026gt; ... end\u003c/code\u003e form lets the prompt content be built from action arguments, so when a \u003ccode\u003eprompt\u003c/code\u003e action\u0027s text incorporates request data, that attacker-controlled text is compiled and run as an EEx template (Elixir source). Content such as \u003ccode\u003e\u0026lt;%= System.cmd(...) %\u0026gt;\u003c/code\u003e therefore executes on the server before any model request is made, requiring no authentication beyond reaching a prompt action. The fix stops evaluating function-supplied prompt content as EEx; only statically configured templates are evaluated.\u003c/p\u003e\n\u003cp\u003eThis issue affects ash_ai: from 0.1.0 before 1.0.0.\u003c/p\u003e"
},
{
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"type": "text/markdown",
"value": "Improper Control of Generation of Code (Code Injection) vulnerability in ash-project ash_ai allows a remote, unauthenticated client to execute arbitrary Elixir code.\n\n`AshAi.Actions.Prompt` evaluates prompt content through `EEx.eval_string/2`. The documented `prompt: fn input, context -\u003e ... end` form lets the prompt content be built from action arguments, so when a `prompt` action\u0027s text incorporates request data, that attacker-controlled text is compiled and run as an EEx template (Elixir source). Content such as `\u003c%= System.cmd(...) %\u003e` therefore executes on the server before any model request is made, requiring no authentication beyond reaching a prompt action. The fix stops evaluating function-supplied prompt content as EEx; only statically configured templates are evaluated.\n\nThis issue affects ash_ai: from 0.1.0 before 1.0.0."
}
],
"value": "Improper Control of Generation of Code (Code Injection) vulnerability in ash-project ash_ai allows a remote, unauthenticated client to execute arbitrary Elixir code.\n\nAshAi.Actions.Prompt evaluates prompt content through EEx.eval_string/2. The documented prompt: fn input, context -\u003e ... end form lets the prompt content be built from action arguments, so when a prompt action\u0027s text incorporates request data, that attacker-controlled text is compiled and run as an EEx template (Elixir source). Content such as \u003c%= System.cmd(...) %\u003e therefore executes on the server before any model request is made, requiring no authentication beyond reaching a prompt action. The fix stops evaluating function-supplied prompt content as EEx; only statically configured templates are evaluated.\n\nThis issue affects ash_ai: from 0.1.0 before 1.0.0."
}
],
"impacts": [
{
"capecId": "CAPEC-242",
"descriptions": [
{
"lang": "en",
"value": "CAPEC-242 Code Injection"
}
]
}
],
"metrics": [
{
"cvssV4_0": {
"Automatable": "NOT_DEFINED",
"Recovery": "NOT_DEFINED",
"Safety": "NOT_DEFINED",
"attackComplexity": "LOW",
"attackRequirements": "PRESENT",
"attackVector": "LOCAL",
"baseScore": 8.9,
"baseSeverity": "HIGH",
"privilegesRequired": "NONE",
"providerUrgency": "NOT_DEFINED",
"subAvailabilityImpact": "HIGH",
"subConfidentialityImpact": "HIGH",
"subIntegrityImpact": "HIGH",
"userInteraction": "NONE",
"valueDensity": "NOT_DEFINED",
"vectorString": "CVSS:4.0/AV:L/AC:L/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H",
"version": "4.0",
"vulnAvailabilityImpact": "HIGH",
"vulnConfidentialityImpact": "HIGH",
"vulnIntegrityImpact": "HIGH",
"vulnerabilityResponseEffort": "NOT_DEFINED"
},
"format": "CVSS",
"scenarios": [
{
"lang": "en",
"value": "GENERAL"
}
]
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-94",
"description": "CWE-94 Improper Control of Generation of Code (\u0027Code Injection\u0027)",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-08-31T15:08:21.204Z",
"orgId": "6b3ad84c-e1a6-4bf7-a703-f496b71e49db",
"shortName": "EEF"
},
"references": [
{
"tags": [
"vendor-advisory",
"related"
],
"url": "https://github.com/ash-project/ash_ai/security/advisories/GHSA-2g59-hg7m-qc83"
},
{
"tags": [
"related"
],
"url": "https://cna.erlef.org/cves/CVE-2026-77956.html"
},
{
"tags": [
"related"
],
"url": "https://osv.dev/vulnerability/EEF-CVE-2026-77956"
},
{
"tags": [
"patch"
],
"url": "https://github.com/ash-project/ash_ai/commit/e9948254b5659c1143b73dc2f59f457931e64514"
}
],
"source": {
"discovery": "EXTERNAL"
},
"title": "EEx template evaluation of prompt content in AshAi enables remote code execution"
}
},
"cveMetadata": {
"assignerOrgId": "6b3ad84c-e1a6-4bf7-a703-f496b71e49db",
"assignerShortName": "EEF",
"cveId": "CVE-2026-77956",
"datePublished": "2026-08-31T00:56:26.440Z",
"dateReserved": "2026-08-30T17:30:01.409Z",
"dateUpdated": "2026-08-31T16:01:41.879Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
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- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
Vulnerability timeline
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.