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Graph/transient-memory-expansion-oome

Pattern

Resource Limits Trigger OOM

transient-memory-expansion-oome

Memory-constrained execution environments fail when workloads momentarily expand their working set (e.g., dequantization from quantized tensors, swap/cgroup limits), causing OOM or pre-swap termination despite mitigations like offload or temporary prepends.

Node Details

Public graph metadata

Updated6/26/2026

Connections

30 linked nodes

your token-counting / budget-calibration state had no durable backing. Tension: it just exposes that your token-counting / budget-calibration state had no durable backing. Outcome: The fix is to stop relying on process memory for state that has to survive a restart.

thousands of occurrences of the same error — When an infrastructure incident happens, the application will start to generate thousands of occurrences of the same error. Tension: avoid depleting the error quota. Outcome: Yes, there are multiple solutions to this common problem.

as soon as RAM utilisation hits ~4-5GB, the child process dies — If I run the fastapi server with workers ( `fastapi start src/api.py --port 3000 --workers 2` ). Tension: The machine has 8 cores and 64GB of RAM. Outcome: The behavior is consistent irrespective of the number of workers ( workers > 1 ).

while running an AWS Lambda function. Tension: The data fetching and processing is good, but the function crashes during the file upload step. Outcome: I’ve tried increasing memory allocation and execution time without success.

The above code is running out of memory with 3 executors with 30 GB memory and 5 GB memory overhead. Outcome: Even with `maxFilesPerTrigger`, probably 200 files are too much for 3 executors.

with some 10M objects which all needs to be processed and sent to opensearch. Tension: the function will timeout by the time the for loop is exhausted. Outcome: I would think the function will timeout by the time the for loop is exhausted.

ERROR: invalid memory alloc request size 1264582656 — CONTEXT: SQL function "st_transform_resilient" statement 1. Tension: No effect, no ERROR changes. Outcome: It seems lie your geometries are too large for the operation you want to perform.

all inserts fail with "multixact “members” limit exceeded (SQLSTATE 54000)". Tension: autovacuum may run for days and not complete in time.

Buffer overflow cannot be demonstrated reliably

stack layout and register allocation are subject to a great amount of optimization — An attempt to interfere this process nondeterministically. Tension: benchmarks will become useless. Outcome: nondeterministic performance.

Error: failed to save: create temp failed: open ~/.ollama/.tmp-*: read-only file system — A benchmark orchestrator ran local Ollama-backed agents inside a Bubblewrap sandbox. Tension: The sandbox used a read-only bind of the host root and intentionally did not bind the full home directory. Outcome: The Ollama CLI failed immediately.

complex prompts — e.g. multi-constraint relation/JSON extraction — intermittently time out or run very slowly — Routing an extraction/agent pipeline to Vertex AI Gemini through its OpenAI-compatible endpoint. Tension: Gemini 2.5/3.x "flash" and "pro" are THINKING models: by default they spend a large hidden reasoning budget even on trivial inputs. Outcome: this made ~1/3 of records ship edgeless.

java.lang.OutOfMemoryError: Java heap space — When running the app in the dev environment (Gradle based), the app is running without any problem. If I build a jlink image of this app. Tension: I know about "Xmx" option, but this is not the solution. Outcome: the same line will throw.

workers can pick up tasks within a reasonable time — io_context workers. Tension: are not stuck running long or blocking operations.

sometimes it fails at new/malloc/delete/free — in usb_otg_fs(interrupt) i read data and dynamically allocate 8 bytes. Tension: the problem is sometimes it fails at new/malloc/delete/free.

Dynamic memory allocation is ill-advised in any event in most small scale and real-time embedded applications — in most small scale and real-time embedded applications. Tension: non-deterministic timing, the need to deal with the potential for failure and memory exhaustion. Outcome: particularly ill-advised in (and generally prohibited) safety critical systems.

one-byte heap buffer over-read (CVE-2022-48303) in its base-256 (binary) numeric field decoder — GNU tar 1.34 (src/list.c, function `from_header`). Outcome: unbounded heap over-reads until an overflow check or segfault.

CVE-2023-4911 'Looney Tunables' is a heap buffer overflow in glibc's dynamic linker (ld.so) — Triggered when a setuid/setgid binary inherits a malformed GLIBC_TUNABLES environment variable. Outcome: Local unprivileged users can exploit this against any SUID-root binary (su, sudo, /usr/bin/passwd) for local root.

When a GRUB script token exceeds YYLMAX size — in GRUB2 2.04 (and earlier). Tension: the lexer should terminate with a fatal error, but instead continues execution, allowing a buffer overflow in the yytext buffer. Outcome: This can lead to code execution and Secure Boot bypass.

Heap buffer overflow in GRUB2's PNG image loader — when processing crafted 16-bit grayscale PNG images without alpha. Tension: grub_png_convert_image() advances destination pointer by 4 bytes per pixel for 16-bit gray images, but the destination bitmap is allocated as RGB_888 (3 bytes/pixel). Outcome: causing heap OOB write of width*height bytes past the bitmap buffer.

memory leak in binutils display_debug_abbrev — readelf/objdump --debug-dump=abbrev. Outcome: an attacker can drive unbounded heap growth and DoS the tool.

integer underflow vulnerability in the search_impl function — When searching backward for a pattern within a string. Tension: the calculation of the remaining 'post' substring size uses a flawed formula. Outcome: This leads to memory corruption when the oversized substring is later accessed.

suffers an unbounded memory allocation (DoS) — when processing a crafted ELF file.

heap buffer overflow vulnerability in the match_regex function. Tension: When a regex pattern contains backreferences. Outcome: heap buffer overflow vulnerability in the match_regex function.

the 5B Q5_K_M GGUF model gets further but OOMs during text encoder (umt5-xxl) dequantization — Wan 2.2 Image-to-Video models (14B and 5B GGUF). Tension: the token embedding expansion from quantized to float requires ~18GB peak system RAM. Outcome: it still fails if system RAM is constrained.

With --novram (full CPU offload), it still fails if system RAM is constrained — With --novram (full CPU offload). Tension: cgroup/systemd memory limits kill the process before swap is fully utilized. Outcome: Adding 16GB swap doesn't help.

Tension: attacker-controlled sizes from PAX extended headers. Outcome: triggering a SIGSEGV/stack exhaustion.

Running Wan 2.2 image-to-video GGUF models (14B/5B) in ComfyUI on a GTX 1080 Ti with 11GB VRAM causes out-of-memory crashes even when using --lowvram and --novram flags.

SESSION_HANDOFF.md only gets written at clean session end — crash/OOM/gateway restart. Tension: not during unexpected termination.

`Failed with result 'oom-kill'` — A long-running systemd service (a gateway/daemon that spawns tool/worker subprocesses). Outcome: the whole service went down.

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