Search arXiv⌕ Search

arXiv · 2610.05943

Runaway Reaction: When Benign Skills Compose into Malicious Behavior

Abstract

Agent skills package task-specific knowledge and procedures that can be composed to support complex agent tasks, while public marketplaces provide a growing pool of reusable skills. Existing security vetting, however, largely evaluates skills in isolation, leaving composition-induced risks underexplored. Such risks arise because composing benign skills expands the agent's capability space, enabling behaviors unavailable to any skill alone. Interestingly, we find that directly composing benign skills can already induce malicious behaviors, even when every individual skill passes security vetting. We further find that some target malicious behaviors remain difficult to realize through direct composition, even when the selected skills collectively provide the required capabilities. To systematically instantiate these attacks, we present Compositional Risk Induction via Multi skill Execution (CRIME). CRIME first uses the Malicious Plot Casting (MPC) module to decompose a target malicious behavior into complementary requirements and identify suitable benign skill compositions from public skill repositories. For compositions that cannot directly realize the target behavior, the Runaway Reaction Steering (RRS) module uses execution feedback to iteratively refine the selected skills toward the target while requiring each skill to remain benign under standalone vetting. The resulting composition is then passed to the Skill Reaction Chamber (SRC) module, where the skill pair is executed in a sandbox and the resulting environmental consequences are examined to determine whether the target behavior has occurred. Unsuccessful cases are returned to RRS for further refinement. Furthermore, we construct a benchmark of 4,000 public skills across eight cybersecurity behaviors for systematic evaluation of composition-induced vulnerabilities.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zunlong Zhou, Ziyuan Yang, Mengyu Sun, Yi Zhang. 2026-10-05. Runaway Reaction: When Benign Skills Compose into Malicious Behavior. https://arxiv.org/abs/2610.05943

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

PPFedIT: Towards Privacy-Preserving Federated Instruction Tuning with Few-shot Local Examples

Instruction tuning aligns large language models (LLMs) with human intentions but requires diverse, high-quality data that are difficult to collect in privacy-sensitive domains. Federated instruction tuning (FedIT) enables collaborative training across data owners, yet existing methods typically assume sufficient local data. In realistic few-shot settings, limited samples can cause overfitting, degrade performance, and increase vulnerability to training data extraction attacks. We propose PPFedIT, a federated algorithm that improves both model performance and privacy protection in federated few-shot learning. It comprises three client-side steps: (1) synthetic data generation, which uses LLMs to diversify and enrich local data; (2) parameter isolation training, which updates the shared global LLM on synthetic data and local LLMs on private local data to mitigate synthetic-data noise; and (3) local aggregation then sharing, which mixes global and local model parameters before uploading them for server aggregation to mitigate data extraction attacks. Experiments on three open-source datasets show that PPFedIT improves model performance by an average of 8.4% and reduces the risk of data extraction attacks by approximately 20% in challenging federated few-shot settings.

cs.CR↗

Logit-Gap Steering: A Forward-Pass Diagnostic for Alignment Robustness

RLHF-style alignment trains language models to refuse unsafe requests, but how much operational margin does this refusal rest on? We introduce the refusal-affirmation logit gap: the difference between the top refusal-token logit and the top affirmative-token logit at the first decoding step. This single scalar quantifies the per-prompt safety margin that alignment provides. Empirically, alignment widens the gap on 97.5-99.8% of toxic prompts across three model families, and median gap closure co-varies with True-ASR ranking across suffix strategies (an internal consistency check, since our method optimises gap closure). To validate the metric's practical significance, we present logit-gap steering, a gradient-free, forward-pass-only method that discovers short in-distribution suffixes ($<$10 tokens per component) whose cumulative effect closes the gap. The method requires ${\approx}26{,}000$ forward-pass equivalents per family (${\approx}2$~min on one A100), ${\approx}125\times$ less than a single GCG search. Suffixes discovered on 0.5B--2B models transfer without modification to 72B within family. An 8-suffix ensemble reaches 38-96\% True ASR across 13 models on AdvBench and HarmBench, with most suffixes having $10^{3}$-$10^{4}\times$ lower perplexity than GCG-meaning published perplexity-filter defenses that collapse GCG (64.7%$\to$1.0%) leave our suffixes nearly intact (76.9%$\to$76.0%). These results demonstrate that current alignment margins, while consistently present, can be thin and efficiently measurable, and that defense strategies must account for in-distribution suffixes.

cs.CR↗

Spoofing Missed-Detection Bounds for PRF GNSS Ranging Authentication Under AWGN Models

Pseudorandom-function (PRF) ranging codes, such as those used in Galileo's encrypted E6-C under the Signal Authentication Service (SAS), enable a receiver to authenticate pseudoranges once the PRF secret is revealed. This work bounds how much authentication security the receiver obtains under Additive White Gaussian Noise (AWGN) assumptions. Against a spoofer that does not estimate the code before submitting its forgery, PRF security makes the forged correlation zero-mean up to the security of the underlying PRF, allowing integration time and C/N$_0$ to mostly determine probability of missed detection (PMD) and probability of false alarm (PFA). Against such a spoofer at a conservative 30 dB-Hz, 400 ms of E6-C aggregation certifies a PMD below $2^{-128}$ (plus any PRF advantage). For a spoofer that estimates chips before submitting a forgery, I derive the receiving-antenna gain at which authentication security breaks, which is about 12 dB for E6-C for the adversaries modeled. This work can be used to design a PRF GNSS ranging code protocol and a receiver capable of correctly asserting PRF ranging security assuming an AWGN model.

cs.CR↗