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

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

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

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

LLM-Enabled Open-Source Systems in the Wild: An Empirical Study of Vulnerabilities in GitHub Security Advisories

OWASP-based analysis reveals recurring architectural risk patterns, especially Supply Chain, Excessive Agency, and Prompt Injection, which often co-occur across multiple stages of execution. These results suggest that existing

Systematization of Knowledge: Security and Safety in the Model Context Protocol Ecosystem

taxonomy of risks in the MCP ecosystem, distinguishing between adversarial security threats (e.g., indirect prompt injection, tool poisoning) and epistemic safety hazards (e.g., alignment failures in distributed tool delegation

MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agents

handling. MSB contributes: (1) a taxonomy of 12 attacks including name-collision, preference manipulation, prompt injections embedded in tool descriptions, out-of-scope parameter requests, user-impersonating responses, false-error

CALYREX: Cross-Attention LaYeR EXtended Transformers for System Prompt Anchoring

untrusted user content with equal structural priority -- a mismatch that leaves models vulnerable to prompt injection and instruction erosion over extended contexts. We propose CALYREX (Cross-Attention LaYeR EXtended transformers

Trust in LLM-controlled Robotics: a Survey of Security Threats, Defenses and Challenges

taxonomy of attack vectors, covering topics such as jailbreaking, backdoor attacks, and multi-modal prompt injection. In response, we analyze and categorize a range of defense mechanisms, from formal safety

Sentra-Guard: A Multilingual Human-AI Framework for Real-Time Defense Against Adversarial LLM Jailbreaks

time modular defense system named Sentra-Guard. The system detects and mitigates jailbreak and prompt injection attacks targeting large language models (LLMs). The framework uses a hybrid architecture with FAISS

OpenSec: Measuring Incident Response Agent Calibration Under Adversarial Evidence

OpenSec, a dual-control reinforcement learning (RL) environment that evaluates IR agents under realistic prompt injection scenarios with execution-based scoring: time-to-first-containment (TTFC), evidence-gated action rate

MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrieval

implanting malicious successful experiences into the agent's long-term memory. Unlike traditional prompt injections that are transient, or standard RAG poisoning that targets factual knowledge, MemoryGraft exploits the agent

Policy-as-Prompt: Turning AI Governance Rules into Guardrails for AI Agents

integrated with a human-in-the-loop review process. Evaluations show our system reduces prompt-injection risk, blocks out-of-scope requests, and limits toxic outputs. It also generates auditable