`SandboxPolicy::ReadOnly` previously implied broad read access and could
not express a narrower read surface.
This change introduces an explicit read-access model so we can support
user-configurable read restrictions in follow-up work, while preserving
current behavior today.
It also ensures unsupported backends fail closed for restricted-read
policies instead of silently granting broader access than intended.
## What
- Added `ReadOnlyAccess` in protocol with:
- `Restricted { include_platform_defaults, readable_roots }`
- `FullAccess`
- Updated `SandboxPolicy` to carry read-access configuration:
- `ReadOnly { access: ReadOnlyAccess }`
- `WorkspaceWrite { ..., read_only_access: ReadOnlyAccess }`
- Preserved existing behavior by defaulting current construction paths
to `ReadOnlyAccess::FullAccess`.
- Threaded the new fields through sandbox policy consumers and call
sites across `core`, `tui`, `linux-sandbox`, `windows-sandbox`, and
related tests.
- Updated Seatbelt policy generation to honor restricted read roots by
emitting scoped read rules when full read access is not granted.
- Added fail-closed behavior on Linux and Windows backends when
restricted read access is requested but not yet implemented there
(`UnsupportedOperation`).
- Regenerated app-server protocol schema and TypeScript artifacts,
including `ReadOnlyAccess`.
## Compatibility / rollout
- Runtime behavior remains unchanged by default (`FullAccess`).
- API/schema changes are in place so future config wiring can enable
restricted read access without another policy-shape migration.
With this PR we do not close the unified exec processes (i.e. background
terminals) at the end of a turn unless:
* The user interrupt the turn
* The user decide to clean the processes through `app-server` or
`/clean`
I made sure that `codex exec` correctly kill all the processes
## Summary
Support updating Personality mid-Thread via UserTurn/OverwriteTurn. This
is explicitly unused by the clients so far, to simplify PRs - app-server
and tui implementations will be follow-ups.
## Testing
- [x] added integration tests
- Make Config.model optional and centralize default-selection logic in
ModelsManager, including a default_model helper (with
codex-auto-balanced when available) so sessions now carry an explicit
chosen model separate from the base config.
- Resolve `model` once in `core` and `tui` from config. Then store the
state of it on other structs.
- Move refreshing models to be before resolving the default model
**Change**: Seatbelt now allows file-ioctl on /dev/ttys[0-9]+ even
without the sandbox extension so pre-created PTYs remain interactive
(Python REPL, shells).
**Risk**: A seatbelted process that already holds a PTY fd (including
one it shouldn’t) could issue tty ioctls like TIOCSTI or termios changes
on that fd. This doesn’t allow opening new PTYs or reading/writing them;
it only broadens ioctl capability on existing fds.
**Why acceptable**: We already hand the child its PTY for interactive
use; restoring ioctls is required for isatty() and prompts to work. The
attack requires being given or inheriting a sensitive PTY fd; by design
we don’t hand untrusted processes other users’ PTYs (we don't hand them
any PTYs actually), so the practical exposure is limited to the PTY
intentionally allocated for the session.
**Validation**:
Running
```
start a python interpreter and keep it running
```
Followed by:
* `calculate 1+1 using it` -> works as expected
* `Use this Python session to run the command just fix in
/Users/jif/code/codex/codex-rs` -> does not work as expected
# Unified Exec Shell Selection on Windows
## Problem
reference issue #7466
The `unified_exec` handler currently deserializes model-provided tool
calls into the `ExecCommandArgs` struct:
```rust
#[derive(Debug, Deserialize)]
struct ExecCommandArgs {
cmd: String,
#[serde(default)]
workdir: Option<String>,
#[serde(default = "default_shell")]
shell: String,
#[serde(default = "default_login")]
login: bool,
#[serde(default = "default_exec_yield_time_ms")]
yield_time_ms: u64,
#[serde(default)]
max_output_tokens: Option<usize>,
#[serde(default)]
with_escalated_permissions: Option<bool>,
#[serde(default)]
justification: Option<String>,
}
```
The `shell` field uses a hard-coded default:
```rust
fn default_shell() -> String {
"/bin/bash".to_string()
}
```
When the model returns a tool call JSON that only contains `cmd` (which
is the common case), Serde fills in `shell` with this default value.
Later, `get_command` uses that value as if it were a model-provided
shell path:
```rust
fn get_command(args: &ExecCommandArgs) -> Vec<String> {
let shell = get_shell_by_model_provided_path(&PathBuf::from(args.shell.clone()));
shell.derive_exec_args(&args.cmd, args.login)
}
```
On Unix, this usually resolves to `/bin/bash` and works as expected.
However, on Windows this behavior is problematic:
- The hard-coded `"/bin/bash"` is not a valid Windows path.
- `get_shell_by_model_provided_path` treats this as a model-specified
shell, and tries to resolve it (e.g. via `which::which("bash")`), which
may or may not exist and may not behave as intended.
- In practice, this leads to commands being executed under a non-default
or non-existent shell on Windows (for example, WSL bash), instead of the
expected Windows PowerShell or `cmd.exe`.
The core of the issue is that **"model did not specify `shell`" is
currently interpreted as "the model explicitly requested `/bin/bash`"**,
which is both Unix-specific and wrong on Windows.
## Proposed Solution
Instead of hard-coding `"/bin/bash"` into `ExecCommandArgs`, we should
distinguish between:
1. **The model explicitly specifying a shell**, e.g.:
```json
{
"cmd": "echo hello",
"shell": "pwsh"
}
```
In this case, we *do* want to respect the model’s choice and use
`get_shell_by_model_provided_path`.
2. **The model omitting the `shell` field entirely**, e.g.:
```json
{
"cmd": "echo hello"
}
```
In this case, we should *not* assume `/bin/bash`. Instead, we should use
`default_user_shell()` and let the platform decide.
To express this distinction, we can:
1. Change `shell` to be optional in `ExecCommandArgs`:
```rust
#[derive(Debug, Deserialize)]
struct ExecCommandArgs {
cmd: String,
#[serde(default)]
workdir: Option<String>,
#[serde(default)]
shell: Option<String>,
#[serde(default = "default_login")]
login: bool,
#[serde(default = "default_exec_yield_time_ms")]
yield_time_ms: u64,
#[serde(default)]
max_output_tokens: Option<usize>,
#[serde(default)]
with_escalated_permissions: Option<bool>,
#[serde(default)]
justification: Option<String>,
}
```
Here, the absence of `shell` in the JSON is represented as `shell:
None`, rather than a hard-coded string value.
second attempt to fix this test after
https://github.com/openai/codex/pull/6884. I think this flakiness is
happening because yield_time is too small for a 10,000 step loop in
python.
Thread through an `exit_notify` tokio `Notify` through to the
`UnifiedExecSession` so that we can return early if the command
terminates before `yield_time_ms`.
As Codex review correctly pointed out below 🙌 we also need a
`exit_signaled` flag so that commands which finish before we start
waiting can also exit early.
Since the default `yield_time_ms` is now 10s, this means that we don't
have to wait 10s for trivial commands like ls, sed, etc (which are the
majority of agent commands 😅)
---------
Co-authored-by: jif-oai <jif@openai.com>
# External (non-OpenAI) Pull Request Requirements
Before opening this Pull Request, please read the dedicated
"Contributing" markdown file or your PR may be closed:
https://github.com/openai/codex/blob/main/docs/contributing.md
If your PR conforms to our contribution guidelines, replace this text
with a detailed and high quality description of your changes.
Include a link to a bug report or enhancement request.
- This PR is to make it on path for truncating by tokens. This path will
be initially used by unified exec and context manager (responsible for
MCP calls mainly).
- We are exposing new config `calls_output_max_tokens`
- Use `tokens` as the main budget unit but truncate based on the model
family by Introducing `TruncationPolicy`.
- Introduce `truncate_text` as a router for truncation based on the
mode.
In next PRs:
- remove truncate_with_line_bytes_budget
- Add the ability to the model to override the token budget.
## Summary
- update documentation, example configs, and automation defaults to
reference gpt-5.1 / gpt-5.1-codex
- bump the CLI and core configuration defaults, model presets, and error
messaging to the new models while keeping the model-family/tool coverage
for legacy slugs
- refresh tests, fixtures, and TUI snapshots so they expect the upgraded
defaults
## Testing
- `cargo test -p codex-core
config::tests::test_precedence_fixture_with_gpt5_profile`
------
[Codex
Task](https://chatgpt.com/codex/tasks/task_i_6916c5b3c2b08321ace04ee38604fc6b)
Unified exec isn't working on Linux because we don't provide the correct
arg0.
The library we use for pty management doesn't allow setting arg0
separately from executable. Use the same aliasing strategy we use for
`apply_patch` for `codex-linux-sandbox`.
Use `#[ctor]` hack to dispatch codex-linux-sandbox calls.
Addresses https://github.com/openai/codex/issues/6450
Adds a new ItemStarted event and delivers UserMessage as the first item
type (more to come).
Renames `InputItem` to `UserInput` considering we're using the `Item`
suffix for actual items.
Add proper feature flag instead of having custom flags for everything.
This is just for experimental/wip part of the code
It can be used through CLI:
```bash
codex --enable unified_exec --disable view_image_tool
```
Or in the `config.toml`
```toml
# Global toggles applied to every profile unless overridden.
[features]
apply_patch_freeform = true
view_image_tool = false
```
Follow-up:
In a following PR, the goal is to have a default have `bundles` of
features that we can associate to a model
