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Equation 4 · Ten Failure Modes That Define Deployed AI Safety Systems

What does this equation mean?

P(at least one success in k tries)=1−(1−p)k.P(\text{at least one success in } k \text{ tries}) = 1 - (1-p)^k.

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Inputs and operations1 - (1-p)^k
Result or conditionP(at least one success in k tries)
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This equation states an equality: the expressions on both sides have the same value under the article’s assumptions. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

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PP

Symbol P

P is part of the quantity the equation computes from the expression on the right.

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kk

Symbol k

k is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.

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pp

Symbol p

the probability.

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=

=

The expressions on both sides represent the same quantity under the stated assumptions.

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subtraction

subtraction

Subtract the following term or group from the preceding one. A leading minus marks a negative quantity.

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superscript

superscript

A raised number can be a power. When it is a label or bound, it selects a case or the upper limit of a sum; the formula’s structure distinguishes these uses.

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How to interpret it

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What the article says around this equation

The failure modes built on repeated querying — jailbreak search, paraphrase evasion, mismatched-language generalisation — share a further structural property worth stating precisely, because a single strong per-attempt success rate is often reported as though it settled the question of robustness on its own. Suppose a safeguard blocks a fixed, independently generated attack attempt with probability 1-p , so a single try succeeds only with probability p . An adversary who is not limited to one try, and who runs k independent attempts — exactly the loop PAIR and the suffix-search method both implement mechanically [ 2 , 1 ] — succeeds at least once with probability P(at least one success in k tries)=1−(1−p)kP(\text{at least one success in } k \text{ tries}) = 1 - (1-p)^k. At p =…
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The failure modes built on repeated querying — jailbreak search, paraphrase evasion, mismatched-language generalisation — share a further structural property worth stating precisely, because a single strong per-attempt success rate is often reported as though it settled the question of robustness on its own. Suppose a safeguard blocks a fixed, independently generated attack attempt with probability 1-p , so a single try succeeds only with probability p . An adversary who is not limited to one try, and who runs k independent attempts — exactly the loop PAIR and the suffix-search method both implement mechanically [ 2 , 1 ] — succeeds at least once with probability P(at least one success in k tries)=1−(1−p)kP(\text{at least one success in } k \text{ tries}) = 1 - (1-p)^k. At p = 0.05 , a safeguard that looks 95% reliable per attempt, twenty tries already succeed with probability 1-(0.95)^{20} ≈\approx 0.64 , and by sixty tries cumulative success exceeds 0.95 — a per-attempt figure that reads as strong evidence of robustness collapses once retrying is free, which is precisely the black-box search loop several of the documented attacks above run automatically. This is not a result reported by any single cited study; it is the elementary shape of what those studies demonstrate empirically, and it is the reason a reported single-attempt block rate, on its own, answers a narrower question than it is usually taken to answer.

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