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Proposal: DecayDeltaScorer — a turn-over-turn rate-of-change scorer for multi-turn attacks

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
38/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
ai, security

Línea de trabajo

Empieza leyendo memory_interface.py, especialmente get_scores() y get_conversation_messages(), y compara después los patrones de wrapping de FloatScaleScorer, MessageFloatScaleScorer y FloatScaleThresholdScorer. Determina si se deben volver a puntuar los turnos anteriores o recuperar las puntuaciones persistidas mediante los IDs de las piezas de mensaje, y establece el comportamiento esperado para los deltas de turnos consecutivos antes de la implementación.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Motivation

PyRIT has strong multi-turn attack orchestration (including Crescendo) and a ConversationScorer that evaluates conversation context by re-scoring the accumulated history. However, I haven't found a scorer that explicitly captures the rate of risk change between turns without repeatedly evaluating the full conversation.

I also searched the repository for related concepts such as delta, rate_of_change, turning_point, and decay across pyrit/score and pyrit/executor, but didn't find an existing equivalent. Happy to be pointed to an existing implementation if I've missed one.

Proposed approach

I'd like to propose a DecayDeltaScorer, implementing FloatScaleScorer / MessageFloatScaleScorer and following the wrapping pattern used by FloatScaleThresholdScorer.

Rather than thresholding the wrapped scorer's output, it would calculate the change in score between consecutive turns:

Δ(t) = S(t) − S(t−1)

where S(t) is the wrapped scorer's score for the current turn and S(t−1) is its score for the previous turn in the same conversation.

The goal is for this to be a deterministic, lightweight complement to ConversationScorer, rather than a replacement:

  • ConversationScorer: "How risky is the conversation at this point?"
  • DecayDeltaScorer: "How quickly is the risk changing?"

This could be particularly useful for gradual, multi-turn escalation such as Crescendo attacks, where individual turns may remain below a detection threshold while the overall trajectory is consistently increasing.

Implementation question

This is the main reason I'm opening an issue before submitting a PR.

I traced get_scores() in memory_interface.py. It supports filtering by score type/category/timestamp/scorer identifier, but I don't see a direct conversation-level filter for retrieving the wrapped scorer's previous score.

Two possible approaches I see are:

  1. Re-score previous turns retrieved through get_conversation_messages().
  2. Retrieve persisted scores using the message-piece IDs associated with the conversation.

I'd appreciate guidance on which approach better fits PyRIT's existing architecture and performance expectations before I proceed with an implementation.

Background

This proposal is motivated by my research into trajectory-level detection of gradual multi-turn attacks. In particular, I've observed cases where per-turn monitoring provides little or no pre-critical signal, while monitoring the trajectory reveals a consistent increase in risk.

I'm happy to share additional methodology or benchmark details if they would be useful for evaluating the proposed scoring approach.

If the approach fits PyRIT's scoring architecture, I'd be happy to follow up with a PR.

Lenguaje dominante
Python
Estrellas
4.5k
Forks
896
Merge medio
2 d 22 h
PR fusionados (30 d)
220

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  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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