CVE-2026-105752: vLLM: Harmony tool continuations drop `cache_salt` — restoring a cross-tenant prefix-cache membership oracle

Affected

- Ecosystem / package: pip / vllm - Affected versions: vLLM ≤ 0.25.1 (confirmed on 0.25.1, commit 752a3a504485). The lower bound predates 0.25.1; maintainers can confirm how far back the tool-continuation re-submission has omitted the salt.

Summary

On the GPT-OSS "Harmony" path (POST /v1/responses), a request that uses a built-in or MCP tool runs as a multi-turn loop: after each tool call vLLM re-renders the full next-turn Harmony prompt and re-submits it to the engine. Turn 1 correctly carries request.cachesalt, but the tool-continuation re-submission rebuilds the engine input via tokensinput(tokenids) with no cachesalt. The continuation prefix is therefore cached in the global unsalted namespace even though the caller opted into salting. A second tenant who can guess the low-entropy post-tool history submits the reconstructed continuation (unsalted) and reads exact per-turn cached-token counts from the Responses usage — restoring the prompt-membership oracle that cachesalt is documented to prevent.

Silently dropping a preserved salt after the supported tool workflow is enabled is a broken isolation control: the caller enabled salting and every turn should stay isolated, but continuation turns leak into the shared cache.

This is distinct from GHSA-4qjh-9fv9-r85r (CVE-2025-46570): that advisory is the prefix-cache membership oracle for which cachesalt is the documented mitigation, and its PR-17045 fix does not close this site — the Harmony tool continuation silently drops the preserved salt, caching in the unsalted namespace and leaking exact cachedtokensperturn counts from a different sink (the Responses serving continuation, not general TTFT timing).

Affected code

Links pinned to the confirmed commit 752a3a504485 (v0.25.1):

- The drop (sink): vllm/entrypoints/openai/responses/serving.py#L712-L713 — tokenids = context.renderforcompletion() then engineinput = tokensinput(tokenids), with no cachesalt. - Correct turn-1 call for contrast: vllm/entrypoints/openai/responses/serving.py#L755 — tokensinput(prompttokenids, cachesalt=request.cachesalt). - tokensinput stores the salt only if passed: vllm/inputs/engine.py#L51-L66 (if cachesalt is not None: inputs["cachesalt"] = cachesalt). - The engine request copies only the current input's salt: vllm/v1/engine/inputprocessor.py#L380 (cachesalt=decoderinputs.get("cachesalt") → None for the continuation). - Prefix-cache hashing keys on the salt only when present: vllm/v1/core/kvcacheutils.py#L560-L561 ([request.cachesalt] if (starttokenidx == 0 and request.cachesalt) else []). - The oracle the attacker reads: vllm/entrypoints/openai/responses/serving.py#L909 (cachedtokensperturn). - The documented control being defeated: vllm/entrypoints/openai/responses/protocol.py#L235 (cachesalt field).

The tool-continuation re-submission rebuilds the engine input with no cachesalt:

python vllm/entrypoints/openai/responses/serving.py Lines 711-715 if isinstance(context, HarmonyContext): tokenids = context.renderforcompletion() engineinput = tokensinput(tokenids)

samplingparams.maxtokens = maxmodellen - len(tokenids)

Contrast with the correct turn-1 call, which does preserve the caller's salt:

python vllm/entrypoints/openai/responses/serving.py Lines 754-755 prompttokenids = renderforcompletion(messages) engineinput = tokensinput(prompttokenids, cachesalt=request.cachesalt)

tokensinput stores the salt on the engine input only when it is passed, so the continuation input carries none and lands in the unsalted namespace:

python vllm/inputs/engine.py Lines 51-66 def tokensinput( prompttokenids: list[int], , prompt: str | None = None, cachesalt: str | None = None, ) -> TokensInput: """ Construct [TokensInput][vllm.inputs.engine.TokensInput] from optional values. """ inputs = TokensInput(type="token", prompttokenids=prompttokenids)

if prompt is not None: inputs["prompt"] = prompt if cachesalt is not None: inputs["cachesalt"] = cachesalt

Impact

An authenticated tenant of a shared deployment can recover whether a guessed post-tool prompt or history was processed by another tenant, with exact cached-token counts rather than noisy latency — the exact prompt-membership oracle cachesalt is documented to prevent. It defeats the multi-user prefix-cache isolation guarantee for salted Harmony tool sessions.

Preconditions: a GPT-OSS Harmony model on /v1/responses; prefix caching enabled (default); an operator-enabled built-in or MCP tool server; the victim sets cachesalt and triggers at least one tool continuation; and the attacker can reconstruct the post-tool history closely enough to match the token prefix. The AC:H metric reflects that guessable-history precondition.

Suggested Fix

Propagate request.cachesalt into every Harmony (and Parsable) tool-continuation re-submission — at the continuation call site call tokensinput(tokenids, cachesalt=request.cachesalt), mirroring the correct turn-1 call. Carry the salt on the HarmonyContext (thread the originating request into the context) so no continuation path can omit it:

diff vllm/entrypoints/openai/responses/serving.py if isinstance(context, HarmonyContext): tokenids = context.renderforcompletion() - engineinput = tokensinput(tokenids) + engineinput = tokensinput( + tokenids, + cachesalt=( + context.request.cachesalt + if context.request is not None + else None + ), + )

with HarmonyContext.init gaining a request: ResponsesRequest | None = None parameter (stored as self.request) that createresponses passes when constructing the context. The continuation prefix is then cached in the victim's salted namespace, mirroring turn 1.

Suggested regression test: assert cachedtokensperturn == 0 for a different-salt probe against a salted victim continuation (the four-way control from the proof of concept).

Credit

Reported by: Patch the Planet (Trail of Bits + OpenAI collaboration)

This vulnerability was discovered using GPT-5.5-Cyber as part of the Patch the Planet security initiative.

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Proposed fix: a fix for this issue is proposed in a public pull request: https://github.com/vllm-project/vllm/pull/51818