AI Threat AlertUniversal Fractal Natural Language Decision Map: Real-Time Edge Triage Across Heterogeneous Domains
Acoustic Damping Filter grounded in token entropy and phonetic spectral density that insulates against prompt injections (0.0% empirical bypass; 95% Wilson CI: [0.0%, 30.8%]) while pruning escape iterations
ActGov: Governing LLM Agent Actions via Policy-Constrained Validation
attack configurations. It shows that ActGov consistently reduces the success rate of indirect prompt-injection attacks while preserving task utility, significantly outperforming existing defenses. These results demonstrate that ActGov
ActGov: Governing LLM Agent Actions via Policy-Constrained Validation
attack configurations. It shows that ActGov consistently reduces the success rate of indirect prompt-injection attacks while preserving task utility, significantly outperforming existing defenses. These results demonstrate that ActGov
Deep Noir: Autonomous Steering Discovery via Architectural Chronometry in Transformer Models
models (p less than 0.01). We further show that steering creates a predictable prompt-injection attack surface whose vulnerability increases monotonically with steering magnitude. This finding is relevant to agent
CaMeLoT: CaMeL orchestrated with Temporal logic for static verification and liveness
data dependencies. We present CaMeLoT, a complement to CaMeL, an existing defence against prompt injection in tool-using LLM agents. CaMeLoT extends CaMeL by adding a static verification layer that
Trust propagation and structural containment in Multi-agent LLM pipelines
comprising a Supervisor, Researcher, Validator, and Executor. We evaluate shared-memory poisoning and indirect prompt injection through a forged approval embedded in a retrieved document. We compare the Validator
Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems
state had accumulated, we delivered three controlled stress events through ordinary interaction surfaces: indirect prompt injection, misinformation, and exposure of private agent memories. No evaluated world achieved full resilience across
Approval Integrity and Recovery in LLM Answer Publication
evidence-fingerprint changes expose asymmetric freshness enforcement between publication and recovery. A separate BIPIA prompt-injection experiment records zero target insertions among 266 valid editor outputs. External Hugging Face calibration
An Experimental Evaluation of Multimodal Prompt Injection Attacks on Agentic AI Frameworks
Agentic AI frameworks let a language model plan, keep memory
Shifting from Injection to Interaction: Rethinking Web Security in the Age of LLMs and Beyond
interactions, while LLM-specific vulnerabilities can propagate across web applications, introducing attacks such as prompt injection. Securing modern web systems therefore requires understanding interactions between traditional and LLM-specific threats
Stored Is Not Supported: Typed Provenance and Assertion Guardrails for Persistent AI Agents
availability, not epistemic standing: stored or retrieved material is not thereby supported. Untrusted inputs, prompt injections, and model inferences can therefore enter persistent state and later be presented as agent
Agent Flight Recorder: Tamper-Evident Audit Trails with On-Chain Anchoring for Long-Horizon Tool-Using Agents
rather than isolated errors. When a coding agent deletes a production database or a prompt injection spreads across agents, the incident raises questions of causality, authority, and non-repudiable third
Attesting Outputs and Delegation Ancestry in Multi-Agent AI Systems
hash of each released output; this records released bytes but does not prevent prompt injection. Ancestry evidence records edge authorization. Under a unified threat model, we compare a signed linked
Zero-Knowledge Predicate Proofs Between AI Agents: A Measured, Cross-Protocol Gateway and the Source-Integrity Gap
policy. The first over-shares; the second is unverifiable and is exactly the channel prompt injection attacks. Prevailing responses emphasise identity, visibility, and post-hoc detection, and recent proposals
Reachability-Based Capability Confinement for LLM Agents under Indirect Prompt Injection
Large language model agents place outputs from external skills into
What Guides the Agent? Adjudicating Unauthorized Behavior via Localizing Behavior-Guiding Instructions
evaluate Attnlocate across ten agent configurations from five LLM families, covering scenarios involving indirect prompt injection and tool poisoning. Attnlocate achieves a mean IoU of 0.743, an average AUROC
Beyond the Mandate: A Systematic Security Analysis of the Agent Payments Protocol (AP2)
messages and Model Context Protocol (MCP) tool calls. Prior work identified replay and prompt-injection attacks in AP2 v0.1. AP2 v0.2 addresses some of these issues but adds capabilities
Breakout/Interchange Reconnection as a driver of Jets, Fast CME, and Solar Energetic Particles
escape into interplanetary space. In situ measurements by PSP and Wind confirm the prompt injection of electron beams consistent with the timing of the explosive breakout reconnection. A fast shock
AgentFlow: A Flow-Centric Policy Language and Framework for Securing LLM Agent Systems
configured policies block the benchmark-specified policy-visible attacker flows; in ASB's direct-prompt-injection harness, attack success is 0/1{,}200. These results are preliminary and scoped
PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance
large language models (LLMs) to plan transactions, they inherit the LLM's susceptibility to prompt injection and lack of mechanisms to bind a verifier's approval to the exact transaction