EEF-CVE-2026-77956 CVE-2026-77956 GHSA-2g59-hg7m-qc83
EEx template evaluation of prompt content in AshAi enables remote code execution
- Severity
- 0.0 / 10
- Unknown · CVSS 4.0
- Exploit chance (EPSS)
- 0.28%
- Chance of exploitation in the next 30 days. Higher than 20.72% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-94 Improper Control of Generation of Code ('Code Injection')
- Fix
1.0.0- Upgrade ash_ai to this version or later
Description
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.
Affected versions
Hex
| Package | Vulnerable | Fixed in |
|---|---|---|
| ash_ai | >= 0.1.0, < 1.0.0 |
1.0.0 |
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
ash_ai Hex → 1.0.0
- mix
{:ash_ai, "~> 1.0.0"} # then: mix deps.update ash_ai
Severity breakdown
CVSS 4.0 0.0
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
- Attack vector
- Local
- Attack complexity
- Low
- Attack requirements
- Present
- Privileges required
- None
- User interaction
- None
- Confidentiality (vulnerable system)
- High
- Integrity (vulnerable system)
- High
- Availability (vulnerable system)
- High
- Confidentiality (subsequent systems)
- High
- Integrity (subsequent systems)
- High
- Availability (subsequent systems)
- High
Weakness
CWE-94: Improper Control of Generation of Code ('Code Injection')
The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.
How to prevent it
- Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
- Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
- Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
- Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
- Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
- Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
- Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].
References
Advisories
Fixes and patches
Packages
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| EEF-CVE-2026-78230 AshAi aggregate tool can read field-policy-protected fields | Not rated yet | Also affects ash_ai |
| EEF-CVE-2026-75760 AshAi vectorize change leaks raw embedding-provider errors, including credentials, in a user-facing error | Not rated yet | Also affects ash_ai |
| EEF-CVE-2026-82580 AshAi echoes raw tool exception messages into the conversation, disclosing internal details | Not rated yet | Also affects ash_ai |
| EEF-CVE-2026-82579 AshAi tool loop never terminates when all tool calls are filtered out, enabling denial of service | Not rated yet | Also affects ash_ai |
| EEF-CVE-2026-82564 Identity tool filter in AshAi accepts operator maps, allowing update or destroy of unidentified records | Not rated yet | Also affects ash_ai |
| EEF-CVE-2026-81315 MCP DNS-rebinding origin check in AshAi is bypassed by a spoofed X-Forwarded-Proto header | Not rated yet | Also affects ash_ai |
| GHSA-26p8-2jq9-3vq9 MLflow vulnerable to remote code execution on unauthenticated servers via third-party scorer deserialization importing caller-placed artifact modules | Critical | Same weakness, CWE-94 |
| GHSA-h3rc-6mm3-gc2m vLLM: Request-controlled mm_processor_kwargs.code_revision allows remote code execution | Critical | Same weakness, CWE-94 |
Credits: Jonatan Männchen / EEF, Peter Ullrich, Zach Daniel / Ash Project
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