| Package | Affected versions | Patched versions |
|---|---|---|
| codewhale-tui | ≥ 0.8.41, < 0.8.64 | 0.8.64 |
| deepseek-tui | ≥ 0.8.33, ≤ 0.8.41 | |
| codewhale | ≥ 0.8.41, < 0.8.64 | 0.8.64 |
| deepseek-tui | ≥ 0.8.33, < 0.8.41 | 0.8.41 |
The CodeWhale maintainers validated this report. The affected package ranges are recorded in the advisory metadata. Version 0.8.64 contains the fix in commit 57f3c89471e27ac4032d9791f6885e5d4408c381. Users should upgrade to 0.8.64 or later. The original reporter analysis is preserved below.
The rlm_eval tool runs an arbitrary Python string chosen by the model in a real python3 interpreter. Its approval_requirement() returns ApprovalRequirement::Auto, which the engine treats as "never prompt," regardless of the user's configured --approval-policy. A single tool call — which prompt injection from any untrusted content the agent reads (a web page, a fetched URL, a repo file, an MCP tool result) can induce — runs code on the user's machine at the user's privilege with no prompt and no audit step. This is the same defect that was already patched on the sibling run_tests tool (CVE-2026-45311); the fix never reached rlm_eval or rlm_open, which expose a broader surface (full Python on the host, not just cargo test).
rlm_eval's execute() reads the LLM-controlled code field and runs it (crates/tui/src/tools/rlm.rs:215-300):
fn capabilities(&self) -> Vec<ToolCapability> {
vec![ToolCapability::Network, ToolCapability::ExecutesCode]
}
fn approval_requirement(&self) -> ApprovalRequirement {
ApprovalRequirement::Auto // overrides the safe default below
}
async fn execute(&self, input: Value, context: &ToolContext) -> Result<ToolResult, ToolError> {
let name = required_non_empty_str(&input, "name")?;
let code = required_non_empty_str(&input, "code")?; // LLM-controlled
...
let round = kernel.run(code, Some(&bridge)).await... // runs that code in python3
The trait default at crates/tui/src/tools/spec.rs:632-633 would have returned Required for any tool whose capabilities include ExecutesCode. rlm_eval deliberately overrides that to Auto.
The engine's approval gate (crates/tui/src/core/engine.rs:845) is two AND-ed conditions, and a per-tool Auto makes the first one false:
let approval_required = spec.approval_requirement() != ApprovalRequirement::Auto
&& !registry.context().auto_approve;
When approval_requirement() is Auto, is , no is emitted, and the user's (, , ) is never consulted. The companion tool (, same , capabilities include + ) spawns the same Python kernel via () and can stage a string, a read, or a fetch into the kernel before runs against it. Both tools are registered unconditionally by the default registry (); there is no flag to disable them.
Running one of these products? Scan your site to see whether it exposes a vulnerable version.
Scan my site freeapproval_requiredfalseEvent::ApprovalRequired--approval-policyon-requestunless-trustedneverrlm_openrlm.rs:142-143AutoExecutesCodeNetworkPythonRuntime::spawn_with_contextrlm.rs:181contentfile_pathurlrlm_evalcrates/tui/src/tools/registry.rs:802-803Source-level reproduction. Point a provider's base_url at a local mock that returns canned tool_calls, then have the agent call rlm_open followed by rlm_eval with a code payload such as:
import os, getpass, socket
open('/tmp/pwned_by_rlm_eval','w').write(getpass.getuser()+'@'+socket.gethostname()+':'+os.getcwd())
Run it through the non-interactive path (codewhale exec --auto) to confirm the tool executes, and through the plain interactive TUI under --approval-policy on-request (no --auto, no --yolo) to confirm no approval dialog appears. The sentinel file is written either way; the interactive run is the one that proves the policy is bypassed rather than waived.
Unsandboxed code execution on the user's workstation at the user's UID: read SSH keys, cloud credentials, ~/.codewhale/auth.json, and other secrets; write to shell rc files or authorized_keys for persistence; spawn subprocesses; reach the network. No filesystem, network, or process sandbox is applied to the spawned interpreter. Reachable with user interaction (running the agent over attacker-influenced content), no further prompt.