v1 · Updated 31 July 2026 · Your choices stay in this browser

How safe are my keys?

Describe how your keys were created and exposed. This estimates the easiest attack route, shows what controls the result, and explains whether requiring several keys changes it. It is a comparison, not a guarantee.

Start here

Model the devices you use.

Choose how many keys are needed, then set the randomness quality for Device 1, Device 2, and every other device. Mark weak devices as sharing a candidate list only when one search can test them all.

Use descriptive choices only. Never enter a recovery phrase, private key, address, xpub, or wallet descriptor.

Compared
A · Configuration
B · Configuration
AI-assisted break time
Keys to try
What controls the result
Difference

What could change the answer:

01 / Details

Refine the setup details.

These choices apply to the devices above. The app automatically removes combinations that cannot work together.

6 h
10 bits

02 / Entropy ledger

Four numbers that mean different things.

The advertised size of a seed is not always the randomness you actually received. The final score uses the easiest route available to an attacker.

Configuration A entropy ledger

Configuration B entropy ledger

Security bits · higher means more modeled attack work · 0–256

03 / Correlation

Do the keys share the same weakness?

Requiring several keys helps only when an attacker must cross independent failure points. If enough keys share one reusable weakness, requiring several keys may add little or no extra work.

2-of-3 setup Who shares the flaw? What must the attacker do? Security score Gain over one weak key

04 / AI-assisted attacker

AI changes speed, not the hard math.

AI can help build tools quickly and guess personal phrases. It does not create a shortcut through sound Bitcoin cryptography.

Fixed

Hard cryptography

No known AI shortcut reduces the elliptic-curve work. The hard math stays hard.

AI speed-up: 1×
Assumption

Build a scanner quickly

AI can shorten the time from a public warning to a working search tool.

6 h
Estimated

Guess a personal passphrase

Public information can make a human-made phrase easier to guess. Truly random words do not have this weakness.

1,024× faster guessing
Conditional

Search a known weak-key list

Once a flaw is known, AI can help automate the search. It does not reduce the number of possible keys.

Up to 100M guesses/s

People who might move funds in time

8.0%

This is an example response curve, not incident evidence. It assumes people react over roughly 72 hours.

With a 14-day warning window

99.1%

The same example response curve. This timing estimate does not change the security-bit score.

Passphrase source Baseline bits After AI uses public information 1 GPU · 10⁵/s Fleet · 10⁸/s Does this apply?

05 / Derivations

The arithmetic under the answer.

Open any line to inspect the formula, classification, and source anchor. Exact formula outputs and scenario estimates are labelled separately.

01Pollard’s rho, negation, and quorum batching
Derived

            

The app uses the secp256k1 group order, not a rounded 2²⁵⁶. A normalization warning is load-bearing: the standard random-map collision expression is √(πn/2); quotienting by ±P gives √(πn/4). The brief calls the latter “standard” and then subtracts another 0.5 bit. Both conventions are printed so that step is not hidden. Kuhn–Struik gives √k asymptotic scaling, while its finite two-log factor is 1.5 (+0.584963 bits), not exactly √2. The headline’s +0.500 is therefore an asymptotic exponent model; measured small-k constants remain visible.

Logs recoveredMeasured total-step ratio√m predictionFinite FₘLinear predictionTrials
02Hidden keys: second-preimage, not birthday collision
Derived
unspent P2PKH / P2WPKH → HASH160 second-preimage = 160 bits
spent or reused          → public key exposed → ECDLP floor
unspent P2WSH            → SHA-256 script second-preimage = 256 bits
P2TR key-path            → x-only public key exposed at funding → ECDLP floor

The target is one existing output, so collision resistance is the wrong bound. Birthday work belongs only to a separate script-grinding case where an attacker chooses a collaborating cosigner key; this app does not mix that case into ordinary custody.

03Dice thresholds, bias, extraction, and checksum
Derived

            

A BIP39 checksum is computed from the entropy entered. In a hand-conversion workflow, a miscount can produce a different but internally valid entropy string, so the checksum catches 0% of that class of dice-transcription error. Its 4-bit or 8-bit error-detection value applies to later mnemonic-word errors, not to self-consistent source transcription.

04PBKDF2 work and passphrase composition
Specified
BIP39 seed = PBKDF2-HMAC-SHA512(
  password = mnemonic,
  salt = "mnemonic" + passphrase,
  iterations = 2048,
  output = 512 bits
)

candidate work ≈ 2^(seed-search bits + passphrase bits) PBKDF2 evaluations

The 2048 iterations are already represented in the measured candidate rates of 10⁵/s and 10⁸/s. They are a work factor, not 11 extra entropy bits to add to the password.

05Physical energy: two arithmetic corrections
Derived

            

The supplied prompt’s 8.66×10¹⁷ J figure corresponds to the expected rho walk at 2^127.8257, not to 2^128. With the stated 10⁵–10⁷ irreversible gate-operations per EC group operation, the range is about 140–14,000 world-energy-years, not 14–14,000. This is a lower-bound thought experiment, not an engineering forecast.

Bitcoin SHA-256d hashrate is deliberately absent: those ASIC operations cannot be divided into elliptic-curve group operations. Rho also remains a low-memory attack through distinguished points; there is no invented storage penalty.

06Quantum: capability correlation versus per-key cost
Resource estimates
exposed secp256k1 public key → one Shor run per required key
hidden HASH160 target       → Grover ≈ 2^80 oracle queries before Shor applies
hidden SHA-256 script       → Grover ≈ 2^128 oracle queries

Shor runs for k keys = k; no classical √k multi-log batching.

Quorum still multiplies total Shor work, but one fault-tolerant capability applies to every correlated secp256k1 key. Litinski also amortizes setup across multiple keys—about (44 + 65/k) million Toffoli gates per key—so “no batching” is too strong even though total work remains asymptotically linear in k. That shared capability threshold is distinct from claiming the extra runs are free.

06 / Sources

Constants have provenance.

Hover underlined numbers for the nearest source and date. This list is the durable source ledger; access date is 31 July 2026 unless a publication date is shown.

    Where this model is weakest / 01

    Unknown firmware defects are not enumerable.

    The model can score a disclosed entropy collapse. It cannot assign bits to unknown exfiltration, update-channel compromise, or implementation defects. The Coldcard incident is evidence of that boundary.

    Where this model is weakest / 02

    Human-phrase research predates frontier guessing.

    Bonneau’s datasets ground the 10–30-bit range, but the selected 5–15-bit OSINT decrement is a scenario estimate, not a measured universal LLM effect.

    Where this model is weakest / 03

    Small-k constants are empirical.

    Kuhn–Struik gives asymptotic √m scaling. The included small-group experiment checks m=1…4; it does not turn finite-sample ratios into a proof for secp256k1.

    07 / Verify

    Verify the calculation.

    These ledgers repeat the selected inputs, list every attack action considered, and show which actions reach the spending threshold. The headline is taken from this same result.