Peak + Accumulation: A Proxy-Level Scoring Formula for Multi-Turn LLM Attack Detection
- URL: http://arxiv.org/abs/2602.11247v1
- Date: Wed, 11 Feb 2026 17:53:41 GMT
- Title: Peak + Accumulation: A Proxy-Level Scoring Formula for Multi-Turn LLM Attack Detection
- Abstract summary: Multi-turn prompt injection attacks distribute malicious intent across multiple conversation turns.<n>No published formula exists for aggregating per-turn pattern scores into a conversation-level risk score at the proxy layer.<n>We propose peak + accumulation scoring -- a formula combining peak single-turn risk, persistence ratio, and category diversity.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multi-turn prompt injection attacks distribute malicious intent across multiple conversation turns, exploiting the assumption that each turn is evaluated independently. While single-turn detection has been extensively studied, no published formula exists for aggregating per-turn pattern scores into a conversation-level risk score at the proxy layer -- without invoking an LLM. We identify a fundamental flaw in the intuitive weighted-average approach: it converges to the per-turn score regardless of turn count, meaning a 20-turn persistent attack scores identically to a single suspicious turn. Drawing on analogies from change-point detection (CUSUM), Bayesian belief updating, and security risk-based alerting, we propose peak + accumulation scoring -- a formula combining peak single-turn risk, persistence ratio, and category diversity. Evaluated on 10,654 multi-turn conversations -- 588 attacks sourced from WildJailbreak adversarial prompts and 10,066 benign conversations from WildChat -- the formula achieves 90.8% recall at 1.20% false positive rate with an F1 of 85.9%. A sensitivity analysis over the persistence parameter reveals a phase transition at rho ~ 0.4, where recall jumps 12 percentage points with negligible FPR increase. We release the scoring algorithm, pattern library, and evaluation harness as open source.
Related papers
- Efficient GPU Retrieval for Semantic Search [11.631441668563802]
We present a policy-aligned retrieval framework for LinkedIn.<n>A lightweight single-slot Stage-1 scorer generates high-recall candidates.<n>On 21K held-out queries, this representation improves offline relevance over a matched-capacity baseline.
arXiv Detail & Related papers (2026-08-29T00:38:27Z) - Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs [4.476374205849672]
Verbalized confidence from black-box language models can serve as an actionable signal for retrieval routing.<n>We evaluate 27,000 policy instances across six QA benchmarks, three model families, and three retrieval policies.
arXiv Detail & Related papers (2026-07-28T11:30:25Z) - From Checker to Forecaster: Code-Owned Evaluation of Model-Generated Strategic Routes Under Delayed Ground Truth [0.0]
RouteCast instantiates the regime for model-generated typed strategic routes.<n>It produces a provisional forecast-ranking; later outcomes evaluate the forecast.<n>It does not establish prospective calibration, causal decision improvement, route-decomposition advantage, or cross-domain validity.
arXiv Detail & Related papers (2026-07-13T00:36:48Z) - Let the Results Speak: A Replication-First Paradigm for LLM Behavioral Benchmarking [22.825786049667602]
We propose a replication-first paradigm to anchor validity to a single human-rater consensus.<n>We certify the instrument via four properties -- reliability across K runs, cross-instrument replication across architecturally distinct judges, historical-footprint calibration via judges from earlier training cohorts, and pre-registered prediction.<n>We test it on emotional accompaniment by letting the self-evolve data-driven across: the dimensions are not pre-stipulated and the procedure stabilizes to a 9-dimension set.
arXiv Detail & Related papers (2026-05-27T03:41:11Z) - Latent Adversarial Detection: Adaptive Probing of LLM Activations for Multi-Turn Attack Detection [0.0]
Multi-turn prompt injection follows a known attack path.<n>We show this attack path leaves an activation-level signature in the model's residual stream.
arXiv Detail & Related papers (2026-04-30T17:16:33Z) - BadCLIP++: Stealthy and Persistent Backdoors in Multimodal Contrastive Learning [73.46118996284888]
Research on backdoor attacks against multimodal contrastive learning models faces two key challenges: stealthiness and persistence.<n>We propose BadCLIP++, a unified framework that tackles both challenges.<n>For stealthiness, we introduce a semantic-fusion QR micro-trigger that embeds imperceptible patterns near task-relevant regions.<n>For persistence, we stabilize trigger embeddings via radius shrinkage and centroid alignment.
arXiv Detail & Related papers (2026-02-19T08:31:16Z) - StealthRL: Reinforcement Learning Paraphrase Attacks for Multi-Detector Evasion of AI-Text Detectors [0.0]
StealthRL is a reinforcement learning framework for AI-text detectors.<n>We train a paraphrase policy against a multi-detector ensemble under realistic adversarial conditions.<n>StealthRL achieves near-zero detection, reduces mean AUROC from 0.74 to 0.27, and attains a 99.9% attack success rate.
arXiv Detail & Related papers (2026-02-09T17:33:46Z) - Replicating TEMPEST at Scale: Multi-Turn Adversarial Attacks Against Trillion-Parameter Frontier Models [0.0]
This study employed the TEMPEST multi-turn attack framework to evaluate ten frontier models from eight vendors across 1,000 harmful behaviors.<n>Six models achieved 96% to 100% attack success rate (ASR), while four showed meaningful resistance, with ASR ranging from 42% to 78%.
arXiv Detail & Related papers (2025-12-08T00:30:40Z) - RaCoT: Plug-and-Play Contrastive Example Generation Mechanism for Enhanced LLM Reasoning Reliability [12.67288560758937]
We propose RaCoT (Retrieval-aware Contrastive-of-Thought), a novel framework that shifts contrastive thinking to the pre-retrieval stage.<n>RaCoT guides the model to proactively focus on the critical details that determine answer divergence"
arXiv Detail & Related papers (2025-10-26T15:06:44Z) - Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments [0.0]
Federated Learning (FL) in 5G and edge network environments face severe security threats from adversarial clients.<n>This paper introduces Hybrid Reputation Aggregation (HRA), a novel robust aggregation mechanism designed to defend against adversarial behaviors in FL without prior knowledge of the attack type.<n>HRA combines geometric anomaly detection with momentum-based reputation tracking of clients.
arXiv Detail & Related papers (2025-09-22T17:18:59Z) - VAULT: Vigilant Adversarial Updates via LLM-Driven Retrieval-Augmented Generation for NLI [15.320553375828045]
VAULT is a fully automated adversarial RAG pipeline that uncovers and remedies weaknesses in NLI models.<n>VAULT consistently outperforms prior in-context adversarial methods by up to 2.0% across datasets.
arXiv Detail & Related papers (2025-08-01T14:22:54Z) - Trigger without Trace: Towards Stealthy Backdoor Attack on Text-to-Image Diffusion Models [70.03122709795122]
Backdoor attacks targeting text-to-image diffusion models have advanced rapidly.<n>Current backdoor samples often exhibit two key abnormalities compared to benign samples.<n>We propose Trigger without Trace (TwT) by explicitly mitigating these consistencies.
arXiv Detail & Related papers (2025-03-22T10:41:46Z) - Exploring Response Uncertainty in MLLMs: An Empirical Evaluation under Misleading Scenarios [49.53589774730807]
Multimodal large language models (MLLMs) have recently achieved state-of-the-art performance on tasks ranging from visual question answering to video understanding.<n>We reveal a response uncertainty phenomenon: twelve state-of-the-art open-source MLLMs overturn a previously correct answer in 65% of cases after receiving a single deceptive cue.
arXiv Detail & Related papers (2024-11-05T01:11:28Z) - AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning [93.77763753231338]
Adversarial Contrastive Prompt Tuning (ACPT) is proposed to fine-tune the CLIP image encoder to extract similar embeddings for any two intermediate adversarial queries.
We show that ACPT can detect 7 state-of-the-art query-based attacks with $>99%$ detection rate within 5 shots.
We also show that ACPT is robust to 3 types of adaptive attacks.
arXiv Detail & Related papers (2024-08-04T09:53:50Z) - Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models [79.76293901420146]
Large Language Models (LLMs) are employed across various high-stakes domains, where the reliability of their outputs is crucial.
Our research investigates the fragility of uncertainty estimation and explores potential attacks.
We demonstrate that an attacker can embed a backdoor in LLMs, which, when activated by a specific trigger in the input, manipulates the model's uncertainty without affecting the final output.
arXiv Detail & Related papers (2024-07-15T23:41:11Z) - Automatically measuring speech fluency in people with aphasia: first
achievements using read-speech data [55.84746218227712]
This study aims at assessing the relevance of a signalprocessingalgorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency.
arXiv Detail & Related papers (2023-08-09T07:51:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.