Use After Free in Fastapi with Hmac Signatures

Use After Free occurs when memory that has been deallocated is later accessed. In the context of FastAPI applications that verify HMAC signatures, this can arise through unsafe handling of request data, temporary buffers, or objects that are reused after being released. When HMAC verification logic relies on mutable buffers or caches that are not properly isolated, an attacker may be able to influence what data occupies freed memory, leading to information disclosure or altered verification outcomes.

Consider a FastAPI endpoint that accepts a JSON payload and an X-Signature header containing an HMAC. If the application deserializes the body into a mutable structure, computes the HMAC over a raw byte slice, and then retains a reference to that slice in a cache or background task, the underlying memory may be freed and repurposed between requests. Because FastAPI relies on asynchronous request handling and background tasks, the window during which freed memory is still referenced can be non-deterministic. An attacker who can trigger allocations that coincide with this window may co-locate controlled data in the same memory region, causing the signature verification to operate on attacker-controlled bytes instead of the original request content.

Another scenario involves the use of string interning or byte caching in Python. When computing HMAC, it is common to normalize inputs by encoding strings to bytes. If these byte representations are cached and later reused without recomputation, and if the cache is not properly invalidated, the same cached bytes may be used for different logical requests. This can result in signature validation that appears correct while actually operating on stale or forged data, bypassing intended integrity checks.

In the context of the 12 parallel security checks run by middleBrick, Use After Free would appear under Property Authorization and Input Validation. The scanner analyzes how request data flows through the application and whether references to transient objects persist beyond their intended lifetime. Because HMAC verification requires precise control over which bytes are signed, any mismatch between the bytes used for signing and those used for verification can lead to authentication bypass or integrity failure.

Real-world attack patterns that mirror these issues include CVE-2021-28831-style authorization bypasses where object reuse leads to signature validation on altered data. While middleBrick does not perform exploit testing, it highlights such risks by correlating runtime behavior with OpenAPI/Swagger specifications, including $ref resolution across complex schemas that may describe cached or mutable structures.

To mitigate Use After Free and related integrity issues when using HMAC signatures in FastAPI, ensure that bytes used for signing are immutable for the duration of verification and that no stale references are retained. Below are concrete, working code examples that demonstrate secure handling.

Secure HMAC verification with immutable copies

Always compute the HMAC over a fresh bytes copy and avoid retaining references to request body buffers.

from fastapi import FastAPI, Request, HTTPException, Header
import hmac
import hashlib

app = FastAPI()

SECRET_KEY = b"super-secret-key-change-in-production"

def verify_signature(body: bytes, signature: str) -> bool:
    computed = hmac.new(SECRET_KEY, body, hashlib.sha256).hexdigest()
    return hmac.compare_digest(computed, signature)

@app.post("/webhook")
async def webhook_endpoint(
    request: Request,
    x_signature: str = Header(..., alias="X-Signature")
):
    # Read body as bytes once and keep a stable copy
    body_bytes = await request.body()
    if not verify_signature(body_bytes, x_signature):
        raise HTTPException(status_code=401, detail="Invalid signature")
    # Process verified payload safely
    return {"status": "ok"}

Avoid caching raw request bytes

If you must cache request data, store a deep copy or serialized representation rather than raw buffers that may be reused by the runtime.

import copy
import json
from fastapi import FastAPI, Request

app = FastAPI()

request_cache = {}

@app.post("/items")
async def create_item(request: Request):
    # Copy the body to avoid accidental reuse of mutable internals
    body_bytes = await request.body()
    body_copy = copy.deepcopy(body_bytes)
    request_cache[request.headers.get("id")] = body_copy
    # Compute HMAC on the original bytes, not the cached copy
    return {"cached_size": len(body_copy)}

Use structured models with explicit fields

Prefer Pydantic models to avoid implicit byte handling and make data flow explicit. This reduces the surface for object reuse bugs.

from fastapi import FastAPI
from pydantic import BaseModel
import hmac
import hashlib

app = FastAPI()

SECRET_KEY = b"super-secret-key-change-in-production"

class WebhookPayload(BaseModel):
    event: str
    data: dict

def verify_signature(body: bytes, signature: str) -> bool:
    computed = hmac.new(SECRET_KEY, body, hashlib.sha256).hexdigest()
    return hmac.compare_digest(computed, signature)

@app.post("/structured")
async def structured_endpoint(
    payload: WebhookPayload,
    x_signature: str
):
    # Serialize the model to bytes for signing, ensuring stable content
    body_bytes = payload.json().encode("utf-8")
    if not verify_signature(body_bytes, x_signature):
        raise HTTPException(status_code=401, detail="Invalid signature")
    return {"event": payload.event}

General remediation guidance

• Compute HMAC over immutable byte representations and avoid retaining references to request buffers.
• Do not reuse mutable objects or caches for bytes that participate in signature generation.
• Prefer explicit models (e.g., Pydantic) to reduce reliance on raw byte manipulation.
• Validate that signature verification logic does not depend on object identity or memory location.

These practices align with how middleBrick evaluates API security: by tracing data flows and ensuring that authorization checks are based on stable, verified inputs rather than fragile memory management assumptions.