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version: "1.0.0" name: lattice-crypto-attacks description: >- Lattice-based cryptanalysis playbook. Use when attacking RSA via Coppersmith small roots, recovering DSA/ECDSA nonces from bias, solving knapsack problems, or applying LLL/BKZ reduction to cryptographic constructions.

SKILL: Lattice-Based Cryptanalysis — Expert Attack Playbook

AI LOAD INSTRUCTION: Expert lattice techniques for CTF and cryptanalysis. Covers LLL/BKZ reduction, Coppersmith's method (univariate and multivariate), Hidden Number Problem for DSA/ECDSA nonce recovery, knapsack attacks, and NTRU analysis. Base models often fail to construct the correct attack lattice (wrong dimensions, missing scaling factors) or misapply Coppersmith bounds.

0. RELATED ROUTING

rsa-attack-techniques for RSA-specific attacks that use lattice methods (Coppersmith, Boneh-Durfee)
symmetric-cipher-attacks for LCG state recovery via lattice
classical-cipher-analysis when lattice methods apply to classical cipher analysis

Quick application guide

Problem Type	Lattice Technique	Key Parameter
RSA small roots	Coppersmith (LLL on polynomial lattice)	Root bound X < N^(1/e)
RSA small d	Boneh-Durfee (multivariate Coppersmith)	d < N^0.292
DSA/ECDSA nonce bias	Hidden Number Problem → CVP	Bias bits known
Knapsack cipher	Low-density lattice attack	Density < 0.9408
LCG truncated output	CVP on recurrence lattice	Unknown bits per output
Subset sum	LLL reduction on knapsack lattice	Element size vs count
NTRU key recovery	Lattice reduction on NTRU lattice	Dimension and key size

1. LATTICE FUNDAMENTALS

1.1 Definitions

A lattice L is the set of all integer linear combinations of basis vectors:

L = { a₁·b₁ + a₂·b₂ + ... + aₙ·bₙ | aᵢ ∈ ℤ }

where b₁, ..., bₙ are linearly independent vectors in ℝᵐ.

Key problems:

SVP (Shortest Vector Problem): Find the shortest non-zero vector in L
CVP (Closest Vector Problem): Given target t, find v ∈ L closest to t
SVP is NP-hard in general, but LLL finds an approximately short vector in polynomial time

1.2 Lattice Quality Metrics

Determinant: det(L) = |det(B)| where B is the basis matrix
Gaussian heuristic: shortest vector ≈ √(n/(2πe)) · det(L)^(1/n)

2. LLL ALGORITHM

2.1 What LLL Does

Takes a lattice basis B and produces a reduced basis B' where:

Vectors are nearly orthogonal
First vector is approximately short (within 2^((n-1)/2) factor of SVP)
Runs in polynomial time: O(n^5 · d · log³ B) where d = dimension, B = max entry size

2.2 SageMath Usage

python

# SageMath
M = matrix(ZZ, [
    [1, 0, 0, large_value_1],
    [0, 1, 0, large_value_2],
    [0, 0, 1, large_value_3],
    [0, 0, 0, modulus],
])
L = M.LLL()
# Short vectors in L reveal the solution
short_vector = L[0]  # first row is typically shortest

2.3 Python (fpylll)

python

from fpylll import IntegerMatrix, LLL
n = 4
A = IntegerMatrix(n, n)
# Fill matrix A...
A[0] = (1, 0, 0, large_value_1)
A[1] = (0, 1, 0, large_value_2)
A[2] = (0, 0, 1, large_value_3)
A[3] = (0, 0, 0, modulus)
LLL.reduction(A)
print(A[0])  # shortest vector

3. BKZ (BLOCK KORKINE-ZOLOTAREV)

3.1 Comparison with LLL

Property	LLL	BKZ-β
Quality	2^((n-1)/2) approximation	2^(n/(β-1)) approximation
Speed	Polynomial	Exponential in β
Block size	Fixed (2)	Configurable β
Best for	Quick reduction	High-quality reduction

3.2 Usage

python

# SageMath
M = matrix(ZZ, [...])
L = M.BKZ(block_size=20)  # β = 20
# fpylll
from fpylll import BKZ
BKZ.reduction(A, BKZ.Param(block_size=20))

Rule of thumb: start with LLL, increase to BKZ if needed. BKZ block size 20-40 is usually sufficient for CTF.

4. COPPERSMITH'S METHOD

4.1 Univariate Case

Given f(x) ≡ 0 (mod N) with small root |x₀| < X, find x₀.

Bound: X < N^(1/d) where d = degree of f.

python

# SageMath — built-in small_roots
N = ...
R.<x> = PolynomialRing(Zmod(N))
f = x^3 + a*x^2 + b*x + c  # known polynomial
roots = f.small_roots(X=2^100, beta=1.0, epsilon=1/30)

Parameters:

X: upper bound on the root
beta: N = p^beta (beta=1.0 for modular root of N itself; beta=0.5 for root mod unknown factor p ≈ √N)
epsilon: smaller = better results but slower (try 1/30 to 1/100)

4.2 Stereotyped Message Attack (RSA)

python

# SageMath
n, e, c = ...  # RSA parameters
known_msb = ...  # known upper portion of message
R.<x> = PolynomialRing(Zmod(n))
f = (known_msb + x)^e - c
# x represents the unknown lower bits
X = 2^(unknown_bit_count)
roots = f.small_roots(X=X, beta=1.0)
if roots:
    m = known_msb + int(roots[0])

4.3 Partial Key Exposure (Factor p)

Known MSBs of p: p = p_known + x where x is small.

python

# SageMath
n = ...
p_known = ...  # known upper bits of p
R.<x> = PolynomialRing(Zmod(n))
f = p_known + x
roots = f.small_roots(X=2^unknown_bits, beta=0.5)
# beta=0.5 because p ≈ √n
if roots:
    p = p_known + int(roots[0])
    q = n // p

4.4 Multivariate Coppersmith (Howgrave-Graham)

For f(x, y) ≡ 0 (mod N):

No polynomial-time algorithm guaranteed
Heuristic methods work in practice
Used in Boneh-Durfee for RSA small d

python

# SageMath — Boneh-Durfee
# e*d ≡ 1 (mod phi) where phi = (p-1)(q-1)
# Rewrite: e*d = 1 + k*((n+1) - (p+q))
# Let x = k, y = (p+q), both small relative to n
R.<x, y> = PolynomialRing(ZZ)
A = (n + 1) // 2
f = 1 + x * (A + y)  # mod e
# Build shift polynomials and construct lattice
# Apply LLL to find small (x₀, y₀)

5. HIDDEN NUMBER PROBLEM (HNP) — DSA/ECDSA NONCE RECOVERY

5.1 Problem Statement

Given: signatures (rᵢ, sᵢ) where nonces kᵢ have known bias (leaked MSBs or LSBs).

DSA equation: s = k⁻¹(H(m) + xr) mod q

Rearranged: k = s⁻¹(H(m) + xr) mod q

If partial bits of k are known: reduces to CVP on a lattice.

5.2 Attack Setup

python

# SageMath
def ecdsa_nonce_attack(signatures, q, known_bits, bit_position='msb'):
    """
    signatures: list of (r, s, hash, known_nonce_bits)
    q: curve order
    known_bits: number of known bits per nonce
    """
    n = len(signatures)
    # Build lattice
    B = 2^(q.nbits() - known_bits)  # bound on unknown part
    M = matrix(QQ, n + 2, n + 2)
    for i in range(n):
        r_i, s_i, h_i, a_i = signatures[i]
        t_i = Integer(inverse_mod(s_i, q) * r_i % q)
        u_i = Integer(inverse_mod(s_i, q) * h_i % q)
        M[i, i] = q
        M[n, i] = t_i
        M[n+1, i] = u_i - a_i  # a_i = known nonce bits
    M[n, n] = B / q
    M[n+1, n+1] = B
    # LLL reduction
    L = M.LLL()
    # Find row containing the private key x
    for row in L:
        x_candidate = Integer(row[n] * q / B) % q
        # Verify x_candidate against one signature
        if verify_private_key(x_candidate, signatures[0], q):
            return x_candidate
    return None

5.3 Practical Nonce Bias Sources

Source	Leaked Bits	Required Signatures
MSB bias (always 0)	1 bit	~100 signatures
k generated with wrong length	Variable	~50 signatures
Timing side channel	1-4 bits	20-100 signatures
Insecure PRNG	Many	Few
Reused nonce (k₁ = k₂)	All	2 signatures

For reused nonce (simplest case):

python

def ecdsa_reused_nonce(r, s1, s2, h1, h2, q):
    """Recover private key when nonce k is reused."""
    # s1 - s2 = k⁻¹(h1 - h2) mod q  (since r is same)
    k = ((h1 - h2) * inverse_mod(s1 - s2, q)) % q
    x = ((s1 * k - h1) * inverse_mod(r, q)) % q
    return x, k

6. KNAPSACK / SUBSET SUM ATTACKS

6.1 Low-Density Attack

Knapsack: given weights a₁,...,aₙ and target S, find x₁,...,xₙ ∈ {0,1} such that Σxᵢaᵢ = S.

Density d = n / max(log₂ aᵢ). If d < 0.9408, lattice attack works.

python

# SageMath
def knapsack_lattice(weights, target):
    """Solve subset sum via LLL lattice attack."""
    n = len(weights)
    # Build lattice (Lagarias-Odlyzko style)
    N = ceil(sqrt(n) / 2)  # scaling factor
    M = matrix(ZZ, n + 1, n + 1)
    for i in range(n):
        M[i, i] = 1
        M[i, n] = N * weights[i]
    M[n, n] = N * target
    # Alternative: CJLOSS embedding
    M2 = matrix(ZZ, n + 1, n + 2)
    for i in range(n):
        M2[i, i] = 1
        M2[i, n + 1] = N * weights[i]
    M2[n, n] = 1
    M2[n, n + 1] = N * (-target)
    L = M2.LLL()
    # Look for short vector with entries in {0, 1, -1}
    for row in L:
        if all(v in (0, 1) for v in row[:n]):
            solution = list(row[:n])
            if sum(solution[i] * weights[i] for i in range(n)) == target:
                return solution
    return None

7. NTRU CRYPTANALYSIS

7.1 NTRU Lattice

python

# SageMath
def ntru_lattice_attack(h, q, N):
    """
    Construct NTRU lattice for key recovery.
    h = public key polynomial (mod q)
    q = modulus
    N = dimension
    """
    # NTRU lattice:
    # | qI  0 |
    # | H   I |
    # where H is the circulant matrix of h
    H = matrix(ZZ, N, N)
    for i in range(N):
        for j in range(N):
            H[i, j] = h[(j - i) % N]
    M = block_matrix([
        [q * identity_matrix(N), zero_matrix(N)],
        [H, identity_matrix(N)]
    ])
    L = M.LLL()
    # Short vector in reduced basis = (f, g) private key
    for row in L:
        f = vector(row[:N])
        g = vector(row[N:])
        if f.norm() < q and g.norm() < q:
            return f, g
    return None

8. CONSTRUCTING ATTACK LATTICES — METHODOLOGY

8.1 General Recipe

1. Express the cryptographic problem as:
   "Find small x such that f(x) ≡ 0 (mod N)"
   or "Find x close to target t in some lattice L"
 
2. Choose lattice type:
   ├─ Polynomial lattice → Coppersmith-style
   ├─ Modular lattice → HNP-style CVP
   └─ Knapsack lattice → subset sum / CJLOSS
 
3. Determine dimensions:
   └─ More dimensions = better approximation but slower
 
4. Set scaling factors:
   └─ Balance the rows so short vector has roughly equal entries
   └─ Common: multiply by N/X where X is the root bound
 
5. Apply reduction:
   ├─ LLL first (fast, usually sufficient)
   └─ BKZ if LLL fails (increase block size: 20, 30, 40)
 
6. Extract solution:
   └─ Check reduced basis rows for valid solutions

8.2 Embedding Technique (CVP → SVP)

Transform CVP into SVP by embedding the target into the lattice:

python

# SageMath
def cvp_to_svp(basis_matrix, target, scale=1):
    """Convert CVP to SVP via Kannan's embedding."""
    n = basis_matrix.nrows()
    m = basis_matrix.ncols()
    # Augment matrix
    M = matrix(ZZ, n + 1, m + 1)
    for i in range(n):
        for j in range(m):
            M[i, j] = basis_matrix[i, j]
        M[i, m] = 0
    for j in range(m):
        M[n, j] = target[j]
    M[n, m] = scale  # scaling factor (try 1, then adjust)
    L = M.LLL()
    # Look for row with last entry = ±scale
    for row in L:
        if abs(row[m]) == scale:
            return vector(target) - vector(row[:m]) * (row[m] // abs(row[m]))
    return None

8.3 Dimension Selection Guide

Problem	Typical Dimension	Notes
Coppersmith univariate (degree d)	d × m where m ≈ 1/ε	Larger m = smaller root bound
HNP with n signatures	n + 2	n ≥ known_bits_ratio × q_bits
Knapsack with n weights	n + 1 or n + 2	Depends on density
LCG with n outputs	n + 1	More outputs = easier
Boneh-Durfee	(m+1)(m+2)/2	m = parameter depth

9. DECISION TREE

Lattice approach needed — which construction?
│
├─ RSA-related?
│  ├─ Small unknown part of message → Coppersmith univariate
│  │  └─ Check: unknown_bits < n_bits / e
│  ├─ Partial factor knowledge → Coppersmith mod p
│  │  └─ Use beta=0.5, X=2^unknown_bits
│  ├─ Small private exponent d → Boneh-Durfee
│  │  └─ Check: d < N^0.292
│  └─ Multiple related equations → multivariate Coppersmith
│
├─ DSA/ECDSA-related?
│  ├─ Reused nonce → direct algebraic recovery (no lattice needed)
│  ├─ Partial nonce leakage → HNP → CVP lattice
│  │  └─ Need enough signatures: n ≥ q_bits / leaked_bits
│  └─ Nonce bias → statistical HNP → larger lattice
│
├─ Knapsack / subset sum?
│  ├─ Low density (d < 0.9408) → CJLOSS lattice attack
│  ├─ High density → lattice attack unlikely to work
│  └─ Super-increasing → greedy algorithm (no lattice needed)
│
├─ LCG / PRNG?
│  ├─ Full outputs known → algebraic recovery (no lattice)
│  ├─ Truncated outputs → CVP on recurrence lattice
│  └─ Unknown modulus → use GCD of output differences
│
├─ NTRU?
│  └─ Build circulant lattice → LLL/BKZ for short key vector
│
└─ Custom problem?
   ├─ Express as "find small root of polynomial mod N" → Coppersmith
   ├─ Express as "find lattice point close to target" → CVP
   ├─ Express as "find short vector in lattice" → SVP / LLL
   └─ If none fit → probably not a lattice problem

10. COMMON PITFALLS

Pitfall	Symptom	Fix
Root bound too large	`small_roots()` returns empty	Reduce X, increase epsilon, verify bound satisfies Coppersmith criterion
Wrong scaling	LLL finds irrelevant short vector	Scale columns so target vector has balanced entries
Insufficient dimension	Solution not in reduced basis	Increase m parameter (more shift polynomials)
Wrong beta	Coppersmith doesn't find factor	beta=0.5 for half-size factor, beta=1.0 for full modulus
Too few signatures (HNP)	Lattice attack fails	Collect more signatures with nonce bias
BKZ block size too small	Solution not short enough	Increase block size (try 25, 30, 40)
Integer overflow	SageMath crashes	Use ZZ ring explicitly, avoid mixing QQ and ZZ

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