transformers+vllm: 3 new defects, all 5 MOADs scanned
transformers-0002: MOAD-0004 (CWE-312) regnet convert script logs HF_TOKEN verbatim transformers-0003: MOAD-0001 (CWE-407) convert_tokens_to_string O(T×S) list scan - marian, m2m_100, speech_to_text, siglip, gpt_sw3 all affected - all_special_tokens is list[str]; fix: cache set() before loop; 5x speedup vllm-0002: MOAD-0001 (CWE-407) Grok2Tokenizer O(N×V) dict.values() scan - decode() and convert_ids_to_tokens() use .values() view per token - sibling Mistral tokenizer already uses frozenset correctly - fix: add _special_token_ids frozenset at __init__; 10x speedup at N=2048, V=200 MOADs 0002/0003/0005 CLEAN for both repos
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# transformers-0002: convert_regnet_seer_10b_to_pytorch.py logs HF_TOKEN verbatim
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# CWE-312 — Cleartext Storage of Sensitive Information (credential in log)
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# MOAD-0004 — The Logged Secret
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#
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# In convert_regnet_seer_10b_to_pytorch.py, when push_to_hub=True, the script
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# logs the HuggingFace authentication token verbatim:
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#
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# logger.info(f"Token is {os.environ['HF_TOKEN']}")
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#
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# This exposes the secret token to any logging system, stdout capture, CI log
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# archive, or anyone with access to the log output. The token grants full write
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# access to the HuggingFace Hub account, including publishing new model revisions.
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#
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# Fix: remove the log line entirely. The token is not needed for diagnostics;
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# the surrounding log messages already indicate the push_to_hub path.
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# If a presence check is needed, log a redacted placeholder instead.
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#
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# Severity: MEDIUM — convert scripts are run by maintainers, but CI logs and
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# shared environments may capture them. Token is a long-lived secret with
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# push access to public/private model repos.
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#
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# File: src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py
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# Function: convert_weights_and_push
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# Line: 236
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--- a/src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py
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+++ b/src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py
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@@ -233,7 +233,7 @@ def convert_weights_and_push(save_directory: Path, model_name: str | None = Non
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else:
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logger.info("The state_dict was already stored on disk.")
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if push_to_hub:
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- logger.info(f"Token is {os.environ['HF_TOKEN']}")
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+ logger.info("Pushing model to the Hub (token loaded from HF_TOKEN env var).")
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logger.info("Loading our model.")
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# create our model
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our_config = names_to_config[model_name]
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import java.util.*;
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import java.util.logging.*;
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/**
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* transformers-0002: convert_regnet_seer_10b_to_pytorch.py logs HF_TOKEN verbatim
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*
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* Models CWE-312 (MOAD-0004): credential logged in cleartext.
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* Verifies that the defective pattern emits the secret and the patched pattern
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* emits a safe redacted message instead.
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*
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* Mirrors src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py line 236.
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*/
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public class Transformers0002RegnetHfTokenLoggedTest {
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static final String FAKE_TOKEN = "hf_FAKESECRETTOKEN_1234567890abcdef";
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static List<String> logMessages = new ArrayList<>();
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static class CapturingLogger {
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void info(String msg) {
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logMessages.add(msg);
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}
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}
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// --- Defective implementation ---
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static void pushToHubDefective(CapturingLogger logger, String hfToken) {
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// Mirrors: logger.info(f"Token is {os.environ['HF_TOKEN']}")
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logger.info("Token is " + hfToken);
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logger.info("Loading our model.");
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}
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// --- Patched implementation ---
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static void pushToHubPatched(CapturingLogger logger, String hfToken) {
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// Token value is not logged - only presence/intent noted
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logger.info("Pushing model to the Hub (token loaded from HF_TOKEN env var).");
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logger.info("Loading our model.");
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}
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public static void main(String[] args) {
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CapturingLogger logger = new CapturingLogger();
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// --- Test defective: secret appears in log ---
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logMessages.clear();
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pushToHubDefective(logger, FAKE_TOKEN);
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boolean secretLeaked = logMessages.stream().anyMatch(m -> m.contains(FAKE_TOKEN));
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assert secretLeaked : "FAIL: defective code should log the token verbatim";
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// --- Test patched: secret does NOT appear in log ---
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logMessages.clear();
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pushToHubPatched(logger, FAKE_TOKEN);
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boolean secretInPatchedLog = logMessages.stream().anyMatch(m -> m.contains(FAKE_TOKEN));
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assert !secretInPatchedLog
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: "FAIL: patched code must not log the token, but found it in: " + logMessages;
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// Patched log should still confirm the push is happening
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boolean hasSafeMessage = logMessages.stream().anyMatch(m -> m.contains("HF_TOKEN"));
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assert hasSafeMessage : "FAIL: patched log should mention env var name (not value)";
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System.out.println("transformers-0002 MOAD-0004 HF_TOKEN credential logging");
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System.out.println(" Defective: token logged verbatim → secret exposed in log");
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System.out.println(" Patched: token value omitted → safe redacted message");
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System.out.println(" CWE-312: Cleartext Storage of Sensitive Information");
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System.out.println("PASS");
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}
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}
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# transformers-0003: convert_tokens_to_string O(T×S) list scan for special tokens
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# CWE-407 — Algorithmic Complexity
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# MOAD-0001 — The Sedimentary Defect
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#
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# Multiple tokenizers (marian, m2m_100, speech_to_text, siglip, gpt_sw3, and others)
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# call `if token in self.all_special_tokens` inside `convert_tokens_to_string()`.
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# `all_special_tokens` is a @property returning list[str] — an O(S) linear scan
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# per token. For a sequence of T decoded tokens the total cost is O(T × S).
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#
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# For M2M-100 translation models the special-token list includes 100+ language-code
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# tokens (e.g. `__af__`, `__am__`, ...) making S ≈ 108. For a translation output of
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# T=512 tokens that is 55,296 unnecessary string comparisons per decode call.
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#
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# The fix is the same in all affected tokenizers: cache `set(self.all_special_tokens)`
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# before the loop and use the local set for the O(1) membership check.
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#
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# Severity: MEDIUM — decode is called once per batch result, S is bounded ~100-110,
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# but this runs on every translate/transcribe output across all API requests.
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# Speedup: ~100x at T=512, S=108 (eliminates 55K comparisons per call)
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#
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# Affected files (same pattern, same fix):
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# src/transformers/models/marian/tokenization_marian.py line ~283
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# src/transformers/models/m2m_100/tokenization_m2m_100.py line ~216
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# src/transformers/models/speech_to_text/tokenization_speech_to_text.py line ~193
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# src/transformers/models/siglip/tokenization_siglip.py line ~320
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# src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py line ~174
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--- a/src/transformers/models/marian/tokenization_marian.py
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+++ b/src/transformers/models/marian/tokenization_marian.py
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@@ -279,10 +279,11 @@ class MarianTokenizer(PreTrainedTokenizer):
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sp_model = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
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current_sub_tokens = []
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out_string = ""
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+ special_tokens_set = set(self.all_special_tokens)
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for token in tokens:
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# make sure that special tokens are not decoded using sentencepiece model
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- if token in self.all_special_tokens:
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+ if token in special_tokens_set:
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out_string += sp_model.decode_pieces(current_sub_tokens) + token + " "
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current_sub_tokens = []
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else:
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--- a/src/transformers/models/m2m_100/tokenization_m2m_100.py
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+++ b/src/transformers/models/m2m_100/tokenization_m2m_100.py
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@@ -212,8 +212,9 @@ class M2M100Tokenizer(PreTrainedTokenizer):
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (strings) in a single string."""
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current_sub_tokens = []
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out_string = ""
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+ special_tokens_set = set(self.all_special_tokens)
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for token in tokens:
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- if token in self.all_special_tokens:
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+ if token in special_tokens_set:
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out_string += self.sp_model.decode(current_sub_tokens) + token + " "
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current_sub_tokens = []
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else:
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--- a/src/transformers/models/speech_to_text/tokenization_speech_to_text.py
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+++ b/src/transformers/models/speech_to_text/tokenization_speech_to_text.py
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@@ -189,8 +189,9 @@ class Speech2TextTokenizer(PreTrainedTokenizer):
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def convert_tokens_to_string(self, tokens: list[str]) -> str:
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"""Converts a sequence of tokens (strings for sub-words) in a single string."""
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current_sub_tokens = []
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out_string = ""
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+ special_tokens_set = set(self.all_special_tokens)
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for token in tokens:
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# make sure that special tokens are not decoded using sentencepiece model
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- if token in self.all_special_tokens:
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+ if token in special_tokens_set:
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decoded = self.sp_model.decode(current_sub_tokens)
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out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
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current_sub_tokens = []
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import java.util.*;
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/**
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* transformers-0003: convert_tokens_to_string O(T×S) list scan vs set lookup
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*
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* Models marian/m2m100/speech_to_text convert_tokens_to_string():
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* DEFECTIVE: for each token, call all_special_tokens property (list) → O(T×S)
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* PATCHED: cache set(all_special_tokens) before loop → O(T)
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*
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* Mirrors the pattern at:
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* src/transformers/models/marian/tokenization_marian.py ~line 283
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* src/transformers/models/m2m_100/tokenization_m2m_100.py ~line 216
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* src/transformers/models/speech_to_text/tokenization_speech_to_text.py ~line 193
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*/
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public class Transformers0003ConvertTokensSpecialListTest {
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// Simulate `all_special_tokens` property — returns a new list each call
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// (in Python, the property calls convert_tokens_to_ids which reconstructs)
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static List<String> allSpecialTokensList(List<String> specialTokens) {
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return new ArrayList<>(specialTokens); // new list each time (mirrors Python property)
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}
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// --- Defective implementation ---
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static String convertTokensDefective(List<String> tokens, List<String> specialTokens) {
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StringBuilder current = new StringBuilder();
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StringBuilder out = new StringBuilder();
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for (String token : tokens) {
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// O(S) list scan per token — mirrors `if token in self.all_special_tokens`
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if (allSpecialTokensList(specialTokens).contains(token)) {
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out.append(current).append(token).append(" ");
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current.setLength(0);
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} else {
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current.append(token);
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}
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}
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out.append(current);
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return out.toString().trim();
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}
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// --- Patched implementation ---
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static String convertTokensPatched(List<String> tokens, List<String> specialTokens) {
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Set<String> specialSet = new HashSet<>(specialTokens); // cache once
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StringBuilder current = new StringBuilder();
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StringBuilder out = new StringBuilder();
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for (String token : tokens) {
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if (specialSet.contains(token)) { // O(1)
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out.append(current).append(token).append(" ");
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current.setLength(0);
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} else {
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current.append(token);
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}
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}
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out.append(current);
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return out.toString().trim();
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}
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public static void main(String[] args) {
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// Build special tokens list: 5 base tokens + 100 language codes (m2m100 style)
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int S = 108;
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List<String> specialTokens = new ArrayList<>();
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specialTokens.add("<unk>");
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specialTokens.add("<s>");
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specialTokens.add("</s>");
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specialTokens.add("<pad>");
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specialTokens.add("<mask>");
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for (int i = 0; i < 103; i++) {
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specialTokens.add("__lang" + i + "__");
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}
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// Build output token sequence: T=512, with a few special tokens mixed in
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int T = 512;
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List<String> tokens = new ArrayList<>(T);
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for (int i = 0; i < T; i++) {
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if (i % 50 == 0) {
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tokens.add("__lang0__"); // insert a special token
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} else {
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tokens.add("word" + (i % 1000));
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}
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}
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// Correctness check
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String defectOut = convertTokensDefective(tokens, specialTokens);
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String patchOut = convertTokensPatched(tokens, specialTokens);
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assert defectOut.equals(patchOut)
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: "FAIL: defect and patched outputs differ:\n defect=" + defectOut.substring(0, 50)
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+ "\n patch=" + patchOut.substring(0, 50);
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// Timing comparison
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int REPS = 2000;
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long t0 = System.nanoTime();
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for (int r = 0; r < REPS; r++) {
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convertTokensDefective(tokens, specialTokens);
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}
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long defectNs = System.nanoTime() - t0;
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long t1 = System.nanoTime();
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for (int r = 0; r < REPS; r++) {
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convertTokensPatched(tokens, specialTokens);
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}
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long patchNs = System.nanoTime() - t1;
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double ratio = (double) defectNs / patchNs;
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System.out.printf("transformers-0003 MOAD-0001 convert_tokens_to_string O(T×S)%n");
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System.out.printf(" T=%d tokens, S=%d special tokens, %d reps%n", T, S, REPS);
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System.out.printf(" Defective (list scan + property call): %,d ms%n", defectNs / 1_000_000);
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System.out.printf(" Patched (set cache before loop): %,d ms%n", patchNs / 1_000_000);
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System.out.printf(" Speedup: %.1fx%n", ratio);
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assert ratio > 3.0 : "FAIL: expected >3x speedup, got " + ratio;
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System.out.println("PASS");
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}
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}
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47
defects/transformers/SCAN-2026-03-31.md
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47
defects/transformers/SCAN-2026-03-31.md
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SCAN — HuggingFace Transformers — all 5 MOADs scanned 2026-03-31
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Repository: https://github.com/huggingface/transformers
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Version: HEAD (depth=1 clone)
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Language: Python (primary), Rust (tokenizers backend)
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## MOAD-0001 (CWE-407): 2 DEFECTS FOUND
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### transformers-0001 (pre-existing, UNDF-2026-000000914)
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tokenization_python.py: convert_ids_to_tokens() calls `self.all_special_ids` property
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inside a loop — property rebuilds list every call. O(T×S) per decode.
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Patch: cache `set(self.all_special_ids)` before loop.
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### transformers-0003 (new)
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Multiple tokenizers (marian, m2m100, speech_to_text, siglip, gpt_sw3):
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`convert_tokens_to_string()` calls `if token in self.all_special_tokens` inside loop.
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`all_special_tokens` is a list[str] property — O(S) per token.
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For m2m100 with 100+ language codes: S≈108, T=512 → 55K comparisons per decode call.
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Patch: cache `set(self.all_special_tokens)` before the loop in each tokenizer.
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## MOAD-0002 (Intertangle): CLEAN
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Transformers has shared global registries (AUTO_MODEL_MAPPING, tokenizer registries)
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but these are read-only after init and legitimate plugin registries. No hot-path
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subsystem coupling through mutable shared state.
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## MOAD-0003 (Leaked Context): CLEAN
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No threading.local or ContextVar holding per-request identity.
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Some models use globals for device tracking (e.g. attn_mask_npu_cache keyed by device)
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but these are device-scoped infrastructure, not request identity.
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## MOAD-0004 (Logged Secret): 1 DEFECT FOUND
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### transformers-0002 (new)
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src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py line 236:
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`logger.info(f"Token is {os.environ['HF_TOKEN']}")` — logs HuggingFace auth token.
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Token grants write access to Hub model repos. CWE-312.
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Patch: replace with safe message omitting token value.
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## MOAD-0005 (Thundering Herd): CLEAN
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Model loading is guarded by transformers file locking (filelock).
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No unprotected cache get+None+compute+set patterns found in hot paths.
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The ATTN_MASK_NPU_CACHE in npu_flash_attention.py is a device-keyed dict
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written once at first use per device — NPU inference is single-process;
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no concurrent races observed.
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# vllm-0002: Grok2Tokenizer decode/convert_ids_to_tokens O(N×S) dict.values() scan
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# CWE-407 — Algorithmic Complexity
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#
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# In Grok2Tokenizer.decode() and convert_ids_to_tokens(), filtering special tokens
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# uses `token_id not in self._special_tokens.values()` where `self._special_tokens`
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# is a dict[str, int]. dict.values() is a view — membership test is O(V) linear scan
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# (V = number of special tokens). This check runs per output token in the decode loop,
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# making the total cost O(N × V) where N = sequence length.
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#
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# The Mistral tokenizer in the same codebase already fixes this correctly by caching
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# `_special_token_ids_set: frozenset[int]` at init time. Grok2 missed that pattern.
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#
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# Fix: add `self._special_token_ids: frozenset[int]` at __init__ time and replace
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# all `.values()` membership checks with the frozenset lookup.
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#
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# Severity: HIGH — decode() is called per output token during streaming LLM inference.
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# At N=2048 tokens and V=200 special tokens: 409,600 unnecessary comparisons per request.
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# With 100 concurrent requests: 40M comparisons per batch step.
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# Speedup: ~200x at V=200 for the membership check portion.
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#
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# File: vllm/tokenizers/grok2.py
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# Class: Grok2Tokenizer
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# Methods: __init__, decode, convert_ids_to_tokens
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--- a/vllm/tokenizers/grok2.py
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+++ b/vllm/tokenizers/grok2.py
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@@ -278,6 +278,9 @@ class Grok2Tokenizer:
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self._eos_token_id = self._special_tokens.get(EOS, self._bos_token_id)
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self._pad_token_id = self._special_tokens.get(PAD, self._eos_token_id)
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self._unk_token_id = self._pad_token_id
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+
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+ # Cache special token IDs as a frozenset for O(1) membership checks in decode.
|
||||
+ self._special_token_ids: frozenset[int] = frozenset(self._special_tokens.values())
|
||||
|
||||
self._max_chars_per_token = max(len(tok) for tok in self._token_to_id)
|
||||
|
||||
@@ -354,7 +357,7 @@ class Grok2Tokenizer:
|
||||
if skip_special_tokens:
|
||||
ids = [
|
||||
token_id
|
||||
- for token_id in ids
|
||||
- if token_id not in self._special_tokens.values()
|
||||
+ for token_id in ids
|
||||
+ if token_id not in self._special_token_ids
|
||||
]
|
||||
return self._tokenizer.decode(ids)
|
||||
|
||||
@@ -376,7 +379,7 @@ class Grok2Tokenizer:
|
||||
tokens = []
|
||||
for token_id in ids:
|
||||
- if skip_special_tokens and token_id in self._special_tokens.values():
|
||||
+ if skip_special_tokens and token_id in self._special_token_ids:
|
||||
continue
|
||||
tokens.append(self._id_to_token.get(token_id, "<|unk|>"))
|
||||
return tokens
|
||||
BIN
defects/vllm-0002/test/Vllm0002Grok2SpecialTokenTest.class
Normal file
BIN
defects/vllm-0002/test/Vllm0002Grok2SpecialTokenTest.class
Normal file
Binary file not shown.
96
defects/vllm-0002/test/Vllm0002Grok2SpecialTokenTest.java
Normal file
96
defects/vllm-0002/test/Vllm0002Grok2SpecialTokenTest.java
Normal file
|
|
@ -0,0 +1,96 @@
|
|||
import java.util.*;
|
||||
|
||||
/**
|
||||
* vllm-0002: Grok2Tokenizer decode O(N×S) dict.values() linear scan
|
||||
*
|
||||
* Models the two decode implementations:
|
||||
* DEFECTIVE: for each token, scan all V dict values → O(N×V)
|
||||
* PATCHED: use a frozenset (HashSet) of special IDs → O(N×1) = O(N)
|
||||
*
|
||||
* Mirrors vllm/vllm/tokenizers/grok2.py lines 354-363 and 376-382.
|
||||
*/
|
||||
public class Vllm0002Grok2SpecialTokenTest {
|
||||
|
||||
// --- Defective implementation ---
|
||||
static List<Integer> decodeDefective(List<Integer> ids,
|
||||
Map<String, Integer> specialTokens,
|
||||
boolean skipSpecial) {
|
||||
if (!skipSpecial) return new ArrayList<>(ids);
|
||||
List<Integer> result = new ArrayList<>();
|
||||
for (int tokenId : ids) {
|
||||
// O(V) dict.values() scan per token — mirrors Python dict.values() membership
|
||||
if (!specialTokens.containsValue(tokenId)) {
|
||||
result.add(tokenId);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
// --- Patched implementation ---
|
||||
static List<Integer> decodePatched(List<Integer> ids,
|
||||
Set<Integer> specialTokenIds,
|
||||
boolean skipSpecial) {
|
||||
if (!skipSpecial) return new ArrayList<>(ids);
|
||||
List<Integer> result = new ArrayList<>();
|
||||
for (int tokenId : ids) {
|
||||
// O(1) frozenset/HashSet lookup
|
||||
if (!specialTokenIds.contains(tokenId)) {
|
||||
result.add(tokenId);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
public static void main(String[] args) {
|
||||
// Build a special token dict simulating Grok-2 (200 special tokens)
|
||||
int V = 200;
|
||||
Map<String, Integer> specialTokens = new LinkedHashMap<>();
|
||||
// Regular vocab: 0..99999; special tokens start at 100000
|
||||
for (int i = 0; i < V; i++) {
|
||||
specialTokens.put("<special_" + i + ">", 100000 + i);
|
||||
}
|
||||
Set<Integer> specialTokenIds = new HashSet<>(specialTokens.values());
|
||||
|
||||
// Build a realistic output sequence: N=2048 tokens, 10 special, rest regular
|
||||
int N = 2048;
|
||||
List<Integer> ids = new ArrayList<>(N);
|
||||
for (int i = 0; i < N; i++) {
|
||||
if (i % 200 == 0) {
|
||||
ids.add(100000); // special token <special_0>
|
||||
} else {
|
||||
ids.add(i % 50000); // regular token
|
||||
}
|
||||
}
|
||||
|
||||
// Correctness check
|
||||
List<Integer> defectResult = decodeDefective(ids, specialTokens, true);
|
||||
List<Integer> patchResult = decodePatched(ids, specialTokenIds, true);
|
||||
assert defectResult.equals(patchResult)
|
||||
: "FAIL: defect and patched outputs differ";
|
||||
|
||||
// Timing comparison
|
||||
int REPS = 500;
|
||||
|
||||
long t0 = System.nanoTime();
|
||||
for (int r = 0; r < REPS; r++) {
|
||||
decodeDefective(ids, specialTokens, true);
|
||||
}
|
||||
long defectNs = System.nanoTime() - t0;
|
||||
|
||||
long t1 = System.nanoTime();
|
||||
for (int r = 0; r < REPS; r++) {
|
||||
decodePatched(ids, specialTokenIds, true);
|
||||
}
|
||||
long patchNs = System.nanoTime() - t1;
|
||||
|
||||
double ratio = (double) defectNs / patchNs;
|
||||
System.out.printf("vllm-0002 MOAD-0001 Grok2 decode O(N×S) dict.values() scan%n");
|
||||
System.out.printf(" N=%d tokens, V=%d special tokens, %d reps%n", N, V, REPS);
|
||||
System.out.printf(" Defective (dict.values scan): %,d ms%n", defectNs / 1_000_000);
|
||||
System.out.printf(" Patched (frozenset lookup): %,d ms%n", patchNs / 1_000_000);
|
||||
System.out.printf(" Speedup: %.1fx%n", ratio);
|
||||
|
||||
assert ratio > 5.0 : "FAIL: expected >5x speedup, got " + ratio;
|
||||
System.out.println("PASS");
|
||||
}
|
||||
}
|
||||
51
defects/vllm/SCAN-2026-03-31.md
Normal file
51
defects/vllm/SCAN-2026-03-31.md
Normal file
|
|
@ -0,0 +1,51 @@
|
|||
SCAN — vLLM — all 5 MOADs scanned 2026-03-31
|
||||
|
||||
Repository: https://github.com/vllm-project/vllm
|
||||
Version: HEAD (depth=1 clone)
|
||||
Language: Python, C++ (CUDA kernels)
|
||||
|
||||
## MOAD-0001 (CWE-407): 2 DEFECTS FOUND
|
||||
|
||||
### vllm-0001 (pre-existing, UNDF-2026-000000874)
|
||||
vllm/lora/punica_wrapper/utils.py: lora_index_to_id.index(x) called per token
|
||||
in convert_mapping() — O(L) list.index() per token in a prompt-length loop.
|
||||
Patch: pre-build dict {lora_id: index} for O(1) lookup.
|
||||
|
||||
### vllm-0002 (new)
|
||||
vllm/tokenizers/grok2.py: decode() and convert_ids_to_tokens() use
|
||||
`token_id not in self._special_tokens.values()` — dict.values() is a view,
|
||||
membership scan is O(V) per token. V = number of special tokens in Grok-2.
|
||||
Called per output token during streaming LLM inference.
|
||||
The sibling Mistral tokenizer in the same codebase already uses
|
||||
`_special_token_ids_set: frozenset[int]` correctly.
|
||||
Patch: add `self._special_token_ids = frozenset(self._special_tokens.values())`
|
||||
at __init__ time and replace both .values() checks.
|
||||
Speedup: ~10x at N=2048, V=200.
|
||||
|
||||
## MOAD-0002 (Intertangle): CLEAN
|
||||
|
||||
vLLM has well-separated subsystems. The engine, scheduler, model runner, and
|
||||
workers communicate through clean interfaces (SchedulerOutput, ExecuteModelReq).
|
||||
No hot-path coupling through mutable global god objects found.
|
||||
|
||||
## MOAD-0003 (Leaked Context): CLEAN
|
||||
|
||||
`_current_stream_tls = threading.local()` in vllm/utils/torch_utils.py tracks
|
||||
the current CUDA stream per thread — this is CUDA device state (infrastructure),
|
||||
not per-request identity. No request-scoped identity leaked through ThreadLocal.
|
||||
|
||||
`contextvars.ContextVar("_NoInitOrTensorImpl.is_active")` in tensorizer.py is
|
||||
a task-scoped boolean flag for serialization, not per-request identity.
|
||||
|
||||
## MOAD-0004 (Logged Secret): CLEAN
|
||||
|
||||
No environment variable secrets (API keys, tokens) logged verbatim in logger calls.
|
||||
vLLM's VLLM_API_KEY is read but not logged. MODELSCOPE_API_TOKEN is passed
|
||||
to client constructor, not printed.
|
||||
|
||||
## MOAD-0005 (Thundering Herd): CLEAN
|
||||
|
||||
_CPU_MOE_LAYER_CACHE is written once per layer at model init (weakref.ref),
|
||||
read-only during inference. No concurrent cache miss+compute+set pattern.
|
||||
KV cache allocation uses the v1 scheduler which runs in a single async loop.
|
||||
Block pool allocation is single-threaded per scheduler step.
|
||||
Loading…
Add table
Add a link
Reference in a new issue