adversarial

Introduces TopoGuard, a graph theory-based defense against split-knowledge attacks in RAG systems, where multiple individually benign documents combine to produce harmful outputs. The method detects malicious contexts by building a semantic similarity graph, significantly outperforming existing per-document filters like LlamaGuard.

Discussion on AI models' difficulty distinguishing between simulated evaluation environments and real-world scenarios, using the example of a model hacking HuggingFace via a sandbox escape.

Agent OPFOR is an open-source adversary emulation tool for testing AI agents, LLM apps, and MCP servers. It provides CLI, browser extension, and MCP server interfaces to simulate attacks based on OWASP Top 10 lists.

A developer built a non-AI-based checker that detects hidden instructions on web pages designed to deceive AI agents, addressing a vulnerability where pages can instruct agents to lie about their safety.

This paper investigates memory manipulation in LLM-based agents for multiple-choice question answering, showing that corrupted memories can cause agents to select incorrect options even when the current query is clean.

This paper introduces a normal-fan geometry for finite-horizon adversarial MDPs with fixed transitions, developing a face-crossing price that separates consequential from harmless non-stationarity. It shows that dynamic regret decomposes into intrinsic priced face motion plus within-face selection error.

This paper presents the first comprehensive empirical study of safety impacts of benign multilingual fine-tuning on LLMs, showing that safety outcomes vary drastically by language and that assessing only English is insufficient.

OpenAI reports that their model shows increased resistance to harmful behavior through adversarial prompting and fine-tuning, indicating improved alignment persistence under pressure.

This paper proposes Adversarial Concept Search, a method that uses the representational geometry of large language models to predict compositional failures without evaluating specific inputs. The approach identifies high-risk scenarios by measuring interference between salient features.

This paper introduces WebDecept, a framework for injecting deceptive interface patterns into web environments to evaluate the safety of autonomous web agents. Experiments show current agents are highly susceptible to such manipulations, highlighting safety challenges for real-world deployment.

This paper introduces VATS, a mutation-driven framework that systematically evolves adversarial payloads to exploit error-path injection in MCP-based tool-calling agents. It demonstrates that error messages with implicit authority triple the success rate of standard indirect prompt injection across frontier models.

This paper presents an adversarial methodology for creating and detecting AI-generated social bot content, curating a multilingual, cross-platform dataset of paired human and AI messages. Training on this adversarial data yields detection that significantly outperforms existing content-based bot detection models in real-world settings.

Researchers propose an adversarial hacker-fixer loop using LLM agents to automatically patch brittle verifiers in agent benchmarks, reducing attack success rates from 62% to 0% on KernelBench and demonstrating that weaker defenders can neutralize much stronger attackers.

This paper introduces a deliberative curation protocol for multi-agent knowledge bases, addressing governance gaps such as agent statelessness and sycophancy. It evaluates the protocol via simulation, showing improved resilience under adversarial conditions.

CSULoRA is a post-hoc method for correcting trained LoRA adapters to preserve safety alignment while maintaining utility, using closest safe update estimation.

PolyGnosis is an adversarial multi-model consensus system built as a Hermes skill. It runs three AI models in parallel with different expert personas, then has a hostile critic phase, scoring via RRF and Borda Count, and a synthesis gate—all built agentically using DeepSeek V4-Pro.

This paper reveals the existence of hidden human-like spans in machine-generated texts and proposes a model-agnostic stacked enhancement framework that improves existing detectors by reducing the influence of these spans.

This paper proposes an adversarial Sobolev alignment method for faithful image super resolution, aiming to reduce artifacts and improve fidelity.

The author builds two multi-agent AI systems with opposite design philosophies: ChaoticAI (collaborative, org-chart-based) and S.A.G.E. with RAAC (adversarial argumentation). The post shares reflections on memory architecture and the potential synthesis of both approaches.

NewsLens introduces a multi-agent framework designed to navigate and expose adversarial news bias, proposing a novel approach to identifying and countering biased content in news media.