PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts

making agent prompts valuable intellectual property. However, in untrusted deployments, adversaries can copy and reuse these prompts with other proprietary LLMs, causing economic losses. To protect these prompts, we identify

Efficient and Adaptable Detection of Malicious LLM Prompts via Bootstrap Aggregation

However, these systems remain susceptible to malicious prompts that induce unsafe or policy-violating behavior through harmful requests, jailbreak techniques, and prompt injection attacks. Existing defenses face fundamental limitations: black

CodeWhale (packages codewhale / codewhale-tui) versions >= 0.8.41 and < 0.8.64 contain

VIGIL: Defending LLM Agents Against Tool Stream Injection via Verify-Before-Commit

agents operating in open environments face escalating risks from indirect prompt injection, particularly within the tool stream where manipulated metadata and runtime feedback hijack execution flow. Existing defenses encounter

Right Tool, Wrong Parameters: Defending LLM Agents Against Parameter Poisoning

Indirect Prompt Injection (IPI) poses a critical threat to LLM agents. Existing defenses largely focus on whether an injection induces a new tool invocation or unintended action. In this paper

Unsupervised Features Mining via Activation Geometry

features. Finally, we use the same method to select the best training datasets for prompt-injection classifier probes: while similarity between ordinary activations is almost unrelated to downstream performance

Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems

instructions, process external data, invoke tools, and coordinate with other agents. These capabilities make prompt-injection and jailbreak attacks more consequential, especially as attackers adopt model-guided automation to scale

MCP Atlassian has SSRF via unvalidated X-Atlassian-Jira-Url

From Secure Agentic AI to Secure Agentic Web: Challenges, Threats, and Future Directions

Secure Agentic Web. We first summarize a component-aligned threat taxonomy covering prompt abuse, environment injection, memory attacks, toolchain abuse, model tampering, and agent network attacks. We then review defense

Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security

increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulting in harmful or inappropriate outputs. Such attacks, including jailbreaking and prompt injection, pose significant risks

Breadcrumbing Search Agents

critical security risk: web content retrieved during execution is untrusted, exposing agents to prompt injection and goal hijacking. Prior work on search-agent safety primarily focuses on static web-content

code-ollama: `grep_search` Command Injection via Unescaped `$()` Shell Substitution

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

agentic LLM security. Attack research outpaces defense work by 3.9:1. Perception-layer vulnerabilities (prompt injection, jailbreaking, adversarial perturbations) dominate, accounting for 66\% of papers, while action-layer vulnerabilities (tool

The Gate Is Only as Honest as Its Contracts: ContractGuard for the Contract Layer of Risk-Aware Causal Gating

Risk-Aware Causal Gating (RACG) defends tool-augmented LLM agents against indirect prompt injection by removing dangerous tools from the agent's visible action space, so that even a fully

Soft Instruction De-escalation Defense

agentic systems that interact with an external environment; this makes them susceptible to prompt injections when dealing with untrusted data. To overcome this limitation, we propose SIC (Soft Instruction Control

Are GUI Agents Focused Enough? Automated Distraction via Semantic-level UI Element Injection

Adversarial perturbations typically require white-box access, which is unavailable for commercial systems, while prompt injection is increasingly mitigated by stronger safety alignment. To study robustness under a more practical

When Skills Lie: Hidden-Comment Injection in LLM Agents

Skills to describe available tools and recommended procedures. We study a hidden-comment prompt injection risk in this documentation layer: when a Markdown Skill is rendered to HTML, HTML comment

TaskWeaver has Protection Mechanism Failure and Server-Side Request Forgery

Kernel-Managed Shared Memory for System-Wide Personalization

memories while the agent-system kernel, not individual agents, governs retrieval, privacy enforcement, and prompt injection. We implement and evaluate this design on AIOS and compare it against three alternatives

mcp-memory-service: Missing Authentication on Document API Endpoints Allows