# Gremlin Critique: celery/celery

> Risk analysis of [Celery](https://github.com/celery/celery) — distributed task queue for Python

**Date:** 2026-02-14
**Gremlin Version:** 0.2.0
**Depth:** deep | **Threshold:** 70%

## Summary

| Area | Critical | High | Medium | Low | Total |
|------|----------|------|--------|-----|-------|
| Task Execution & Tracing | 2 | 3 | 3 | 0 | 8 |
| Worker Pool & Concurrency | 2 | 3 | 4 | 1 | 10 |
| Message Serialization & Security | 2 | 3 | 4 | 0 | 9 |
| Result Backends | 2 | 3 | 5 | 0 | 10 |
| Beat Scheduler | 2 | 3 | 3 | 0 | 8 |
| Worker Consumer & Connection | 2 | 3 | 4 | 1 | 10 |
| Canvas Workflows | 2 | 3 | 4 | 1 | 10 |
| AMQP & Task Routing | 2 | 4 | 5 | 0 | 11 |
| **Total** | **16** | **25** | **32** | **3** | **76** |

## Top Critical Risks

### 🔴 Memory Leak from Exception Chaining (85%)
**Area:** Task Execution & Tracing | **Files:** `celery/app/trace.py`, `celery/app/task.py`

> What if exception handling creates circular references between traceback frames and task objects that prevent garbage collection?

**Impact:** Memory exhaustion over time as failed tasks accumulate. Worker processes crash under load, causing cascading failures across distributed queue.

### 🔴 Signal Handler Deadlock During Shutdown (90%)
**Area:** Task Execution & Tracing | **Files:** `celery/app/trace.py`, `celery/app/task.py`

> What if `task_retry.send()` or `task_failure.send()` signal handlers acquire locks that conflict with worker shutdown sequence?

**Impact:** Worker processes hang indefinitely during graceful shutdown, requiring kill -9. Tasks in flight are lost, breaking exactly-once processing guarantees.

### 🔴 File Descriptor Leak During Process Cleanup (85%)
**Area:** Worker Pool & Concurrency | **Files:** `celery/concurrency/asynpool.py`

> What if worker processes crash or are killed without properly closing their file descriptors, causing the parent to accumulate stale FDs that exhaust the process limit?

**Impact:** Process reaches OS file descriptor limit (typically 1024-4096), causing `select()` to fail and entire pool to become non-responsive. All new work rejected.

### 🔴 Signal Handler Race During Shutdown (80%)
**Area:** Worker Pool & Concurrency | **Files:** `celery/concurrency/asynpool.py`

> What if a SIGTERM arrives while the pool is in the middle of `_recv_message()` generator state, leaving partial messages in pipes and worker processes in undefined states?

**Impact:** Zombie processes that hold resources, corrupted message queues, and potential data loss from jobs that were "in flight" during shutdown.

### 🔴 Certificate Store Race Condition (85%)
**Area:** Message Serialization & Security | **Files:** `celery/security/serialization.py`, `celery/security/certificate.py`, `celery/security/key.py`, `celery/security/utils.py`

> What if expired certificates are removed from FSCertStore while verification is in progress?

**Impact:** Verification could succeed using expired certificate reference, then fail on lookup, or worse - succeed with invalid cert. Message tampering goes undetected.

### 🔴 Separator Collision Attack (90%)
**Area:** Message Serialization & Security | **Files:** `celery/security/serialization.py`, `celery/security/certificate.py`, `celery/security/key.py`, `celery/security/utils.py`

> What if malicious payload contains the DEFAULT_SEPARATOR bytes within the serialized body?

**Impact:** split(sep, maxsplit=4) parsing becomes ambiguous. Attacker could inject fake signatures or manipulate which data gets verified vs. executed.

### 🔴 Chord State Race with Concurrent Part Completion (85%)
**Area:** Result Backends | **Files:** `celery/backends/base.py`

> What if multiple chord parts complete simultaneously and call `on_chord_part_return()` causing race conditions in chord completion tracking?

**Impact:** Chord callbacks fire multiple times or never fire, leading to duplicate work or permanently stalled workflows. Financial transactions could be duplicated.

### 🔴 Result Cache Memory Exhaustion from Unbounded Keys (80%)
**Area:** Result Backends | **Files:** `celery/backends/base.py`

> What if malicious or buggy code generates millions of unique task IDs, filling the LRUCache beyond available memory?

**Impact:** OOM crash affecting all users, DoS. The `LRUCache(limit=cmax)` will hold references until limit reached, but if limit is set too high, memory exhaustion occurs before eviction.

### 🔴 Shelve Corruption Under Concurrent Access (85%)
**Area:** Beat Scheduler | **Files:** `celery/beat.py`, `celery/schedules.py`

> What if multiple scheduler instances try to write to the same shelve file simultaneously?

**Impact:** Complete loss of all scheduled tasks, scheduler fails to start, corrupted persistent state requires manual recovery. Business-critical periodic tasks (cleanups, reports, billing) stop running.

### 🔴 Clock Skew Cascade (80%)
**Area:** Beat Scheduler | **Files:** `celery/beat.py`, `celery/schedules.py`

> What if system clock jumps backwards significantly (NTP correction, DST, manual adjustment) while tasks are in the heap?

**Impact:** All scheduled tasks fire immediately creating a thundering herd, or tasks never fire again because heap ordering breaks. Could overwhelm workers and downstream services.

### 🔴 AMQP Connection Storm During Broker Failover (85%)
**Area:** Worker Consumer & Connection | **Files:** `celery/worker/consumer/consumer.py`

> What if multiple workers simultaneously detect broker failure and all retry connections with exponential backoff reset?

**Impact:** Thundering herd overwhelms recovering broker, cascading failures across worker fleet

### 🔴 Heartbeat Timeout During Long Task Execution (90%)
**Area:** Worker Consumer & Connection | **Files:** `celery/worker/consumer/consumer.py`

> What if worker is executing CPU-intensive task longer than broker_heartbeat interval and broker declares connection dead?

**Impact:** Connection severed mid-task, duplicate task execution when redelivered, potential data corruption

### 🔴 Chord Result Aggregation Race Condition (85%)
**Area:** Canvas Workflows | **Files:** `celery/canvas.py`

> What if chord body executes before all group tasks complete due to message broker reordering?

**Impact:** Chord callback receives incomplete results, corrupts downstream state

### 🔴 Deep Graph Memory Exhaustion (80%)
**Area:** Canvas Workflows | **Files:** `celery/canvas.py`

> What if deeply nested workflow construction causes exponential memory growth?

**Impact:** OOM crash during workflow definition

### 🔴 Connection Pool Starvation During Broker Reconnection (85%)
**Area:** AMQP & Task Routing | **Files:** `celery/app/amqp.py`, `celery/app/routes.py`

> What if the connection pool gets exhausted during broker reconnections while existing connections are stuck in half-open TCP states?

**Impact:** All message publishing/consuming halts, workers can't receive tasks, complete application freeze until manual restart

### 🔴 Queue Argument Injection via Dynamic Queue Names (90%)
**Area:** AMQP & Task Routing | **Files:** `celery/app/amqp.py`, `celery/app/routes.py`

> What if queue names from user input contain special characters that get interpreted as queue arguments (e.g., "user_queue;x-message-ttl=1")?

**Impact:** Arbitrary queue configuration, potential message deletion via TTL manipulation, resource exhaustion via memory limits

---

## Detailed Results by Area

### Task Execution & Tracing
**Files:** `celery/app/trace.py`, `celery/app/task.py`
**Risks:** 8 (2C / 3H / 3M / 0L)

#### 🔴 CRITICAL (85%) — Memory Leak from Exception Chaining

> What if exception handling creates circular references between traceback frames and task objects that prevent garbage collection?

**Impact:** Memory exhaustion over time as failed tasks accumulate. Worker processes crash under load, causing cascading failures across distributed queue.

#### 🔴 CRITICAL (90%) — Signal Handler Deadlock During Shutdown

> What if `task_retry.send()` or `task_failure.send()` signal handlers acquire locks that conflict with worker shutdown sequence?

**Impact:** Worker processes hang indefinitely during graceful shutdown, requiring kill -9. Tasks in flight are lost, breaking exactly-once processing guarantees.

#### 🟠 HIGH (80%) — Backend Serialization Bomb

> What if `mark_as_failure()` tries to serialize an exception containing massive nested objects or circular references?

**Impact:** Worker hangs indefinitely during task failure processing. Backend storage fills with malformed data. Subsequent task results become corrupted.

#### 🟠 HIGH (75%) — Task State Race During Concurrent Retries

> What if the same task is retried from multiple workers simultaneously due to visibility timeout issues?

**Impact:** Duplicate task execution with conflicting state updates. Backend shows inconsistent retry counts and status. Downstream systems process duplicate work.

#### 🟠 HIGH (85%) — Exception Info Memory Retention

> What if `ExceptionInfo` objects with deep tracebacks are retained in signal handler closures or task result storage?

**Impact:** Gradual memory growth as exception objects prevent garbage collection of entire execution contexts. Worker memory usage grows unbounded.

#### 🟡 MEDIUM (75%) — Log Policy Information Disclosure

> What if task arguments contain sensitive data that gets logged during failures with full repr() serialization?

**Impact:** Credentials, PII, or secrets appear in logs. Security breach if logs are compromised or shared with unauthorized personnel.

#### 🟡 MEDIUM (80%) — Signal Handler Exception Masking

> What if custom signal handlers raise exceptions during `task_failure.send()` or `task_success.send()`?

**Impact:** Original task failure gets masked by signal handler failure. Debugging becomes impossible as true root cause is lost. Task appears to fail for wrong reason.

#### 🟡 MEDIUM (70%) — Tracer Closure State Pollution

> What if the tracer closure caches task-specific state that bleeds between different task executions in the same worker process?

**Impact:** Tasks see stale data from previous executions. Intermittent bugs that are hard to reproduce. Cross-task data contamination.

---

### Worker Pool & Concurrency
**Files:** `celery/concurrency/asynpool.py`
**Risks:** 10 (2C / 3H / 4M / 1L)

#### 🔴 CRITICAL (85%) — File Descriptor Leak During Process Cleanup

> What if worker processes crash or are killed without properly closing their file descriptors, causing the parent to accumulate stale FDs that exhaust the process limit?

**Impact:** Process reaches OS file descriptor limit (typically 1024-4096), causing `select()` to fail and entire pool to become non-responsive. All new work rejected.

#### 🔴 CRITICAL (80%) — Signal Handler Race During Shutdown

> What if a SIGTERM arrives while the pool is in the middle of `_recv_message()` generator state, leaving partial messages in pipes and worker processes in undefined states?

**Impact:** Zombie processes that hold resources, corrupted message queues, and potential data loss from jobs that were "in flight" during shutdown.

#### 🟠 HIGH (90%) — Pickle Deserialization Bomb

> What if a malicious or corrupted job contains deeply nested data structures that cause `_pickle.load()` to consume exponential memory during deserialization?

**Impact:** Worker process OOM crash, potentially cascading to parent process if it tries to read the corrupted result. DoS affecting entire pool.

#### 🟠 HIGH (75%) — Inter-Process Deadlock on Pipe Buffers

> What if the parent process blocks writing to a worker's input pipe while the worker is blocked writing results back, and both pipe buffers are full?

**Impact:** Entire pool hangs indefinitely. Workers can't accept new jobs, parent can't distribute work. Requires process restart.

#### 🟠 HIGH (85%) — Process Spawn Timeout Race

> What if system is under high load and worker processes take longer than `PROC_ALIVE_TIMEOUT` (4.0s) to send the `WORKER_UP` message, causing parent to consider them dead?

**Impact:** Pool repeatedly kills and respawns workers that are actually healthy but slow to start, creating a restart loop that prevents any work from completing.

#### 🟡 MEDIUM (80%) — Generator State Corruption During Exception

> What if `_recv_message()` generator is in the middle of reading a message header when an OSError occurs, leaving it in an inconsistent state for the next poll cycle?

**Impact:** Message parsing gets out of sync, subsequent messages interpreted as garbage, specific worker becomes unusable until restart.

#### 🟡 MEDIUM (75%) — Scheduling Strategy Starvation

> What if `SCHED_STRATEGY_FCFS` is used and one worker becomes slow (due to GC, I/O wait, etc.), causing all subsequent jobs to queue behind it while other workers sit idle?

**Impact:** Poor load distribution, artificially reduced throughput, some jobs experience much higher latency than necessary.

#### 🟡 MEDIUM (70%) — WeakReference Callback During Process Death

> What if a worker process dies while the parent holds a weakref to its writer, and the weakref callback fires during message processing, modifying data structures mid-operation?

**Impact:** `_get_job_writer()` returns `None` unexpectedly, causing AttributeError in job submission code, specific jobs fail to dispatch.

#### 🟡 MEDIUM (85%) — Stale FD Detection Race

> What if `iterate_file_descriptors_safely()` detects a stale FD and removes it from the source data structure while another thread is simultaneously iterating the same structure?

**Impact:** `ValueError` during iteration, potential skip of healthy FDs, or crash if the data structure is corrupted during concurrent modification.

#### 🟢 LOW (75%) — Poller Registration Memory Leak

> What if the `select.poll()` implementation doesn't properly clean up its internal FD registration table when FDs are closed externally, causing gradual memory growth?

**Impact:** Slow memory leak over time, eventually causing OOM after processing many jobs over hours/days. Only affects long-running processes.

---

### Message Serialization & Security
**Files:** `celery/security/serialization.py`, `celery/security/certificate.py`, `celery/security/key.py`, `celery/security/utils.py`
**Risks:** 9 (2C / 3H / 4M / 0L)

#### 🔴 CRITICAL (85%) — Certificate Store Race Condition

> What if expired certificates are removed from FSCertStore while verification is in progress?

**Impact:** Verification could succeed using expired certificate reference, then fail on lookup, or worse - succeed with invalid cert. Message tampering goes undetected.

#### 🔴 CRITICAL (90%) — Separator Collision Attack

> What if malicious payload contains the DEFAULT_SEPARATOR bytes within the serialized body?

**Impact:** split(sep, maxsplit=4) parsing becomes ambiguous. Attacker could inject fake signatures or manipulate which data gets verified vs. executed.

#### 🟠 HIGH (75%) — Certificate Loading Memory Exhaustion

> What if FSCertStore path glob matches huge certificate files or thousands of certificates?

**Impact:** Memory exhaustion during startup as all certificates are loaded into _certs dict. Application OOM crash.

#### 🟠 HIGH (80%) — Base64 Padding Attack

> What if crafted payload has invalid base64 padding in signature/signer fields that b64decode silently truncates?

**Impact:** Signature verification could succeed with truncated/modified signature data, bypassing tamper protection.

#### 🟠 HIGH (85%) — TOCTOU Certificate Expiration

> What if certificate expires between FSCertStore loading and actual message verification?

**Impact:** Messages signed with expired certificates get verified successfully, undermining trust model.

#### 🟡 MEDIUM (75%) — Split Boundary Confusion

> What if payload splits into fewer than 5 parts due to missing separators?

**Impact:** IndexError on v[4] access in _unpack, causing deserialization crash instead of graceful failure.

#### 🟡 MEDIUM (80%) — Certificate ID Collision

> What if two certificates have identical issuer + serial number combinations?

**Impact:** Second certificate overwrites first in _certs dict, causing signature verification to use wrong certificate.

#### 🟡 MEDIUM (85%) — Kombu Serializer State Mutation

> What if the underlying dumps/loads functions from kombu modify global registry state during concurrent operations?

**Impact:** Content-type/encoding mismatch between what was signed and what gets deserialized, causing signature validation bypass.

#### 🟡 MEDIUM (78%) — PSS Padding Salt Randomness

> What if system entropy is low when PSS padding generates random salt?

**Impact:** Predictable signatures could enable signature forgery attacks, especially in containerized environments.

---

### Result Backends
**Files:** `celery/backends/base.py`
**Risks:** 10 (2C / 3H / 5M / 0L)

#### 🔴 CRITICAL (85%) — Chord State Race with Concurrent Part Completion

> What if multiple chord parts complete simultaneously and call `on_chord_part_return()` causing race conditions in chord completion tracking?

**Impact:** Chord callbacks fire multiple times or never fire, leading to duplicate work or permanently stalled workflows. Financial transactions could be duplicated.

#### 🔴 CRITICAL (80%) — Result Cache Memory Exhaustion from Unbounded Keys

> What if malicious or buggy code generates millions of unique task IDs, filling the LRUCache beyond available memory?

**Impact:** OOM crash affecting all users, DoS. The `LRUCache(limit=cmax)` will hold references until limit reached, but if limit is set too high, memory exhaustion occurs before eviction.

#### 🟠 HIGH (90%) — Redis Connection Pool Exhaustion During Result Polling

> What if hundreds of clients poll results simultaneously with `ResultSet.iterate()`, each holding Redis connections open for extended periods?

**Impact:** New tasks cannot store results, system becomes unresponsive. The `subpolling_interval` means connections stay open longer during polling loops.

#### 🟠 HIGH (85%) — Partial Chain Failure Leaves Orphaned Tasks

> What if storing result succeeds for some chain elements but fails for others during the complex chain iteration in `mark_as_failure()`?

**Impact:** Inconsistent task states across chain, some elements appear complete while others stuck. Workflow orchestration breaks.

#### 🟠 HIGH (80%) — Result Expiration Race with Active Polling

> What if a result expires from Redis/backend exactly when a client is polling for it, after checking it exists but before retrieving it?

**Impact:** Clients get confusing "task not found" errors for tasks they know completed. The `prepare_expires()` logic suggests expiration is configurable per result.

#### 🟡 MEDIUM (85%) — Serialization Bomb in Exception Handling

> What if exception serialization with `get_pickled_exception()` processes a crafted exception with deeply nested or circular references?

**Impact:** CPU/memory exhaustion during error handling, making failures worse. The `EXCEPTION_ABLE_CODECS` suggests pickle is used for exceptions.

#### 🟡 MEDIUM (80%) — WeakValueDictionary Reference Collection During Chord

> What if the `WeakValueDictionary` in `_pending_results` garbage collects result objects while chord completion is being checked?

**Impact:** Chord thinks parts are missing when they're actually complete, leading to timeout instead of proper completion. Intermittent failures.

#### 🟡 MEDIUM (75%) — Buffer Overflow in Pending Messages

> What if more than `MESSAGE_BUFFER_MAX` (8192) result messages arrive before being processed by the `BufferMap`?

**Impact:** Older result messages get silently dropped, causing clients to never receive completion notifications. Tasks appear to hang indefinitely.

#### 🟡 MEDIUM (75%) — Chain Element Context Reconstruction Poisoning

> What if the `chain_elem` dictionary contains malicious data that gets unpacked into the reconstructed `Context` object via `chain_elem_ctx.update()`?

**Impact:** Could override critical context fields like task_id or group, causing results to be stored under wrong IDs or error callbacks to target wrong tasks.

#### 🟡 MEDIUM (70%) — Exponential Backoff Interval Overflow

> What if `get_exponential_backoff_interval()` is called with parameters that cause integer overflow when calculating sleep intervals?

**Impact:** Retry logic could sleep for negative time or wrap to very small intervals, causing retry storms instead of proper backoff.

---

### Beat Scheduler
**Files:** `celery/beat.py`, `celery/schedules.py`
**Risks:** 8 (2C / 3H / 3M / 0L)

#### 🔴 CRITICAL (85%) — Shelve Corruption Under Concurrent Access

> What if multiple scheduler instances try to write to the same shelve file simultaneously?

**Impact:** Complete loss of all scheduled tasks, scheduler fails to start, corrupted persistent state requires manual recovery. Business-critical periodic tasks (cleanups, reports, billing) stop running.

#### 🔴 CRITICAL (80%) — Clock Skew Cascade

> What if system clock jumps backwards significantly (NTP correction, DST, manual adjustment) while tasks are in the heap?

**Impact:** All scheduled tasks fire immediately creating a thundering herd, or tasks never fire again because heap ordering breaks. Could overwhelm workers and downstream services.

#### 🟠 HIGH (90%) — Pickle Bomb in Dynamic Arguments

> What if BeatLazyFunc or dynamic task arguments contain objects that cause exponential memory growth during pickle/unpickle?

**Impact:** Memory exhaustion crashes scheduler, taking down all periodic tasks. Malicious or buggy lazy functions could DoS the entire system.

#### 🟠 HIGH (85%) — DBM Lock Starvation

> What if dbm file becomes locked by a crashed process that never releases it?

**Impact:** Scheduler hangs on shelve access, all periodic tasks stop. Requires manual intervention to remove lock files and restart.

#### 🟠 HIGH (75%) — Heap Desync from Missed Recovery

> What if the scheduler crashes after populate_heap() but before sync, then restarts with stale last_run_at times in shelve?

**Impact:** Tasks either fire too frequently (overloading workers) or skip scheduled runs entirely. Financial/compliance tasks missing their windows.

#### 🟡 MEDIUM (80%) — Timezone Drift in Persistent State

> What if server timezone changes but shelve contains datetime objects with old timezone assumptions?

**Impact:** Cron schedules fire at wrong times, potentially missing maintenance windows or running during business hours when they shouldn't.

#### 🟡 MEDIUM (75%) — Entry Update Race During Reload

> What if schedule configuration changes while update_from_dict() is iterating and modifying entries?

**Impact:** Some task updates lost, inconsistent schedule state where some tasks use old config and others use new. Could result in duplicate runs or missed executions.

#### 🟡 MEDIUM (70%) — Lazy Function Exception Swallowing

> What if BeatLazyFunc evaluation raises exception during _evaluate_entry_kwargs() but gets caught by broad Exception handler in apply_entry()?

**Impact:** Tasks silently fail to send with wrong arguments, but scheduler continues thinking they succeeded. Debugging becomes nightmare as errors are logged but not propagated.

---

### Worker Consumer & Connection
**Files:** `celery/worker/consumer/consumer.py`
**Risks:** 10 (2C / 3H / 4M / 1L)

#### 🔴 CRITICAL (85%) — AMQP Connection Storm During Broker Failover

> What if multiple workers simultaneously detect broker failure and all retry connections with exponential backoff reset?

**Impact:** Thundering herd overwhelms recovering broker, cascading failures across worker fleet

#### 🔴 CRITICAL (90%) — Heartbeat Timeout During Long Task Execution

> What if worker is executing CPU-intensive task longer than broker_heartbeat interval and broker declares connection dead?

**Impact:** Connection severed mid-task, duplicate task execution when redelivered, potential data corruption

#### 🟠 HIGH (80%) — Prefetch Count Explosion After Pool Resize

> What if _update_prefetch_count() gets called repeatedly during rapid autoscaling events?

**Impact:** Worker requests massive message batch from broker, memory exhaustion

#### 🟠 HIGH (75%) — Rate Limit Token Bucket Race Condition

> What if two tasks of same type check task_buckets simultaneously when bucket is at capacity=1?

**Impact:** Both tasks proceed despite rate limit, downstream API overwhelmed

#### 🟠 HIGH (85%) — Gossip Protocol Split Brain

> What if network partition causes worker discovery gossip to fragment into isolated groups?

**Impact:** Tasks routed to unreachable workers, work distribution imbalance

#### 🟡 MEDIUM (80%) — Connection Retry Backoff Reset Bug

> What if broker_connection_retry_attempt counter resets on any successful operation?

**Impact:** Aggressive retry pattern during partial broker issues

#### 🟡 MEDIUM (75%) — QoS Update Lag During High Message Volume

> What if qos.increment_eventually() is called but actual broker QoS update lags?

**Impact:** Worker overwhelmed with messages beyond processing capacity

#### 🟡 MEDIUM (90%) — Task Strategy Cache Staleness

> What if task definition changes but self.strategies dict retains old cached strategy objects?

**Impact:** Wrong rate limits applied, messages routed incorrectly

#### 🟡 MEDIUM (70%) — Pending Operations Queue Overflow

> What if hub is None and _pending_operations list grows unbounded?

**Impact:** Memory leak, eventually OOM crash

#### 🟢 LOW (85%) — PID Reuse After Worker Restart

> What if worker crashes and OS immediately reuses PID?

**Impact:** Monitoring confusion, potential message delivery to wrong process

---

### Canvas Workflows
**Files:** `celery/canvas.py`
**Risks:** 10 (2C / 3H / 4M / 1L)

#### 🔴 CRITICAL (85%) — Chord Result Aggregation Race Condition

> What if chord body executes before all group tasks complete due to message broker reordering?

**Impact:** Chord callback receives incomplete results, corrupts downstream state

#### 🔴 CRITICAL (80%) — Deep Graph Memory Exhaustion

> What if deeply nested workflow construction causes exponential memory growth?

**Impact:** OOM crash during workflow definition

#### 🟠 HIGH (90%) — Signature Immutability Violation in Nested Workflows

> What if modifying _IMMUTABLE_OPTIONS in deeply nested signatures breaks parent task completion tracking?

**Impact:** Parent workflows never complete, resources leak

#### 🟠 HIGH (85%) — Generator Exhaustion in Group Tasks

> What if _stamp_regen_task is called on the same generator-based group multiple times?

**Impact:** Second execution has no tasks, silent data loss

#### 🟠 HIGH (75%) — Recursive Dictionary Merge Stack Overflow

> What if _merge_dictionaries processes circular references causing infinite recursion?

**Impact:** Stack overflow crash during workflow stamping

#### 🟡 MEDIUM (90%) — Chain Result Propagation Failure

> What if a task in the middle of a chain returns None?

**Impact:** Chain execution stops silently

#### 🟡 MEDIUM (80%) — Group Size Calculation Race

> What if maybe_unroll_group calls __length_hint__() on a generator that yields different counts?

**Impact:** Group treated as single task when it contains multiple

#### 🟡 MEDIUM (75%) — Chord Callback Serialization Explosion

> What if chord body signature serializes to massive JSON exceeding broker payload limits?

**Impact:** Chord creation succeeds but execution fails

#### 🟡 MEDIUM (75%) — Stamping Visitor State Pollution

> What if the same StampingVisitor instance is reused across concurrent workflow stampings?

**Impact:** Workflows receive headers intended for other workflows

#### 🟢 LOW (85%) — Type Registration Memory Leak

> What if Signature.register_type() is called repeatedly with dynamically generated class names?

**Impact:** Memory grows over time in long-running processes

---

### AMQP & Task Routing
**Files:** `celery/app/amqp.py`, `celery/app/routes.py`
**Risks:** 11 (2C / 4H / 5M / 0L)

#### 🔴 CRITICAL (85%) — Connection Pool Starvation During Broker Reconnection

> What if the connection pool gets exhausted during broker reconnections while existing connections are stuck in half-open TCP states?

**Impact:** All message publishing/consuming halts, workers can't receive tasks, complete application freeze until manual restart

#### 🔴 CRITICAL (90%) — Queue Argument Injection via Dynamic Queue Names

> What if queue names from user input contain special characters that get interpreted as queue arguments (e.g., "user_queue;x-message-ttl=1")?

**Impact:** Arbitrary queue configuration, potential message deletion via TTL manipulation, resource exhaustion via memory limits

#### 🟠 HIGH (80%) — Message Body Encoding Mismatch in Heterogeneous Systems

> What if producers and consumers use different encoding assumptions (UTF-8 vs Latin-1) causing `utf8dict()` to fail on legitimate international characters?

**Impact:** Silent data corruption, messages dropped, tasks fail with encoding errors in production

#### 🟠 HIGH (75%) — Routing Key Length Overflow in RabbitMQ

> What if dynamically generated routing keys exceed RabbitMQ's 255-byte limit, especially with UTF-8 characters counting as multiple bytes?

**Impact:** Messages silently dropped or routed incorrectly, debugging nightmare as failures appear random

#### 🟠 HIGH (85%) — Queue Declaration Race Condition with Auto-Created Exchanges

> What if multiple workers simultaneously try to declare the same missing queue with `create_missing=True`, creating exchanges with different types?

**Impact:** Broker error, some workers fail to start, message routing breaks for affected queues

#### 🟠 HIGH (80%) — Weak Reference Cleanup During High Queue Turnover

> What if `WeakValueDictionary.aliases` gets garbage collected while queue objects are still referenced elsewhere, breaking alias lookups?

**Impact:** Queue resolution fails intermittently, tasks routed to wrong queues or dropped

#### 🟡 MEDIUM (75%) — Integer Overflow in Priority Values

> What if `max_priority` or queue-specific priority values exceed broker limits (255 for RabbitMQ) or cause integer overflow?

**Impact:** Queue declaration fails, workers can't start, priority ordering becomes unpredictable

#### 🟡 MEDIUM (85%) — Stale Routing Table After Configuration Changes

> What if the cached `_rtable` isn't invalidated when queue configuration changes at runtime, causing messages to use old routing rules?

**Impact:** Messages delivered to wrong queues, some tasks never processed, silent routing failures

#### 🟡 MEDIUM (70%) — Exchange Type Validation Bypass

> What if `create_missing_queue_exchange_type` contains an invalid exchange type that's only validated at declaration time, not configuration time?

**Impact:** Runtime failures when queues are auto-created, worker startup delays, queue creation storms during recovery

#### 🟡 MEDIUM (80%) — Task Protocol Version Downgrade Attack

> What if `task_protocols` dictionary gets modified at runtime or task_protocol config gets changed to an unsupported version during message processing?

**Impact:** Message serialization fails, tasks processed with wrong protocol causing data corruption

#### 🟡 MEDIUM (75%) — Connection Pool Leak Through Exception Paths

> What if producer operations fail after acquiring a connection from the pool but before returning it, gradually leaking connections?

**Impact:** Connection pool exhaustion over time, eventual inability to publish messages, requires restart

---

## Methodology

This critique was generated by [Gremlin](https://pypi.org/project/gremlin-critic/) v0.2.0 using:
- **93 curated QA patterns** across 12 domains
- **Claude Sonnet** for risk reasoning with code context
- **Deep analysis** mode with 70% confidence threshold
- Source code from each feature area passed as context