docs(10_Wiki): Topic_Business/General/Graphic/Programming을 Topics/ 하위로 이동
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id: wiki-2026-0508-side-channel-attack
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title: Side-channel Attack
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Side-channel, Timing Attack, Cache Attack]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.95
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verification_status: applied
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tags: [security, cryptography, hardware, attack]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: c/python
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framework: openssl/numpy
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---
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# Side-channel Attack
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## 매 한 줄
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> **"매 알고리즘 의 정상 output 이 아닌 부수 누출 (시간, 전력, 캐시, EM 방사) 로 secret 추출"**. 매 1996 Kocher 의 timing attack on RSA 가 시초. 매 2018 Spectre/Meltdown 으로 mass awareness. 매 2026 LLM weight extraction, GPU side-channel 까지 확장.
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## 매 핵심
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### 매 카테고리
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- **Timing**: 시간 차이 → key 추출 (RSA, AES, PIN compare).
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- **Power analysis (SPA/DPA)**: 전력 trace → key bits.
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- **EM**: 전자기 방사 → 동일 정보.
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- **Cache (Flush+Reload, Prime+Probe)**: shared L3 cache.
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- **Speculative (Spectre, Meltdown)**: speculative exec leak via cache.
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- **Microarchitectural (LVI, Foreshadow, Zenbleed)**: CPU bug exploit.
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- **Acoustic / Optical**: 매 keyboard sound, monitor flicker.
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- **Software**: padding oracle, error message disclosure.
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### 매 ML / AI 신종
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- **Membership inference**: 매 model 출력 으로 training data 멤버 여부 추론.
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- **Model extraction**: 매 query → weight stealing.
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- **Prompt injection side-channel**: token timing.
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### 매 응용 (defensive)
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1. Constant-time crypto code.
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2. Cache partitioning.
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3. KASLR + KPTI (Meltdown 대응).
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4. Differential privacy (ML).
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## 💻 패턴
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### Timing-vulnerable string compare
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```c
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// VULNERABLE
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int compare_password(const char* a, const char* b, size_t n) {
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for (size_t i = 0; i < n; i++) {
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if (a[i] != b[i]) return 0; // early exit → timing leak
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}
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return 1;
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}
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// SAFE — constant time
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int safe_compare(const uint8_t* a, const uint8_t* b, size_t n) {
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uint8_t diff = 0;
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for (size_t i = 0; i < n; i++) diff |= a[i] ^ b[i];
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return diff == 0;
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}
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```
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### Timing attack demo
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```python
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import time, statistics
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def measure(guess, target):
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samples = []
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for _ in range(1000):
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t0 = time.perf_counter_ns()
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compare_password(guess, target)
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samples.append(time.perf_counter_ns() - t0)
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return statistics.median(samples)
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# Brute force first byte: char with longest median = correct
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for c in range(256):
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guess = bytes([c]) + b'\x00'*15
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print(c, measure(guess, target_secret))
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```
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### Constant-time AES (lookup-free)
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```c
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// Bitsliced implementation — no data-dependent table lookup → no cache leak
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// Reference: bsaes (BearSSL)
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void aes_bitsliced_encrypt(uint64_t state[8], uint64_t rk[88]);
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```
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### Spectre v1 (bounds-check bypass)
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```c
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// VULNERABLE
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if (idx < array_size) {
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y = array2[array1[idx] * 256]; // speculatively executed even if idx large
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}
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// → array1 OOB read → array2 cache state encodes secret
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```
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### Spectre mitigation (LFENCE)
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```c
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if (idx < array_size) {
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__asm__ volatile("lfence" ::: "memory"); // serialize speculation
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y = array2[array1[idx] * 256];
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}
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```
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### Padding oracle (CBC mode)
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```python
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# VULNERABLE: distinguishable error messages
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def decrypt(ciphertext):
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plaintext = aes_cbc_decrypt(ciphertext, key)
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try:
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unpad(plaintext)
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except PaddingError:
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return "Invalid padding" # ← oracle leak
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return "Invalid MAC"
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# SAFE: encrypt-then-MAC (always check MAC first, constant-time)
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```
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### Differential privacy ML defense
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```python
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import opacus
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from torch.utils.data import DataLoader
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privacy_engine = opacus.PrivacyEngine()
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model, optimizer, dl = privacy_engine.make_private(
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module=model, optimizer=optimizer, data_loader=dl,
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noise_multiplier=1.1, max_grad_norm=1.0,
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)
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```
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### Cache flush+reload
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```c
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// Probe shared library page
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clflush(&victim_addr);
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victim_function(); // runs in target process
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uint64_t t0 = rdtsc(); volatile char x = *victim_addr; uint64_t t1 = rdtsc();
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if (t1 - t0 < THRESHOLD) printf("hit — accessed by victim\n");
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Crypto code (key compare, AES) | Constant-time + bitsliced |
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| Web auth | hmac.compare_digest / crypto.timingSafeEqual |
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| Cloud multi-tenant | Cache partitioning + Spectre patches |
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| ML model serving | Output rate-limit + DP training |
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| Embedded HW | Power analysis countermeasures (masking, hiding) |
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**기본값**: constant-time primitives + libsodium / BoringSSL 의 사용.
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## 🔗 Graph
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- 변형: [[Spectre]] · [[Rowhammer]] · [[Timing Attack]]
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- 응용: [[Differential Privacy]]
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## 🤖 LLM 활용
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**언제**: constant-time review, vulnerable code 의 패턴 인식, mitigation suggestions.
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**언제 X**: actual exploit development (legal/ethical line).
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## ❌ 안티패턴
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- **Naive memcmp for secrets**: timing leak.
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- **Data-dependent branch in crypto**: cache + branch predictor leak.
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- **"Roll your own crypto"**: 매 side-channel free 의 어려움.
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- **Verbose error messages**: padding oracle 류.
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## 🧪 검증 / 중복
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- Verified (Kocher 1996, Spectre paper 2018, Intel/AMD advisories).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full side-channel coverage |
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