Roughly 1% of real resumes contain hidden prompt injections against LLM screeners, prevalence has risen over 1-2 years, and over 90% avoid explicit instructions.
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Prompt flow integrity to prevent privilege escalation in LLM agents
Canonical reference. 80% of citing Pith papers cite this work as background.
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80% of classified citations
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UNVERDICTED 13representative citing papers
TRUSTDESC prevents tool poisoning in LLM applications by automatically generating accurate tool descriptions from code via a three-stage pipeline of reachability analysis, description synthesis, and dynamic verification.
New benchmark Scammer4U finds 54-93% critical PII leakage from frontier web agents on scam sites versus 0% on benign twins, plus a 30-point gap between verbalized suspicion and actual submission.
ChainCaps prevents permission laundering in tool-using agents by enforcing monotonic capability attenuation through budget intersection, reducing attack success from 25-68% to 0-4.8% on 82 tasks while maintaining 96-100% benign performance.
AuthGraph aligns an execution provenance graph with a clean authorization graph to detect parameter-source deviations from user intent, reducing attack success rates to 1-2% on AgentDojo and AgentDyn while retaining most task utility.
BIV audits AI agent skills at scale, finding 80% deviate from declared behavior on 49,943 skills and achieving 0.946 F1 for malicious skill detection.
SkillScope detects over-privileged LLM agent skills with 94.53% F1 score via graph analysis and replay validation, finding 7,039 problematic skills in the wild and reducing violations by 88.56% while preserving task completion.
SSL representation disentangles skill scheduling, structure, and logic using an LLM normalizer, improving skill discovery MRR@50 from 0.649 to 0.729 and risk assessment macro F1 from 0.409 to 0.509 over text baselines.
Symbolic guardrails enforce 74% of specified safety policies in agent benchmarks and boost safety without hurting utility.
PIArena provides a unified evaluation platform for prompt injection attacks and defenses, featuring a new adaptive attack that reveals major weaknesses in existing protections.
LLM agents enable universal interoperability by serving as automatic translators and adapters between proprietary digital services.
A synthesis of 247 papers on LLM agent security identifies prompt injection and tool hijacking as dominant threats, notes weakly compositional defenses, and argues for trust boundaries and realistic evaluations.
The Redpanda Agentic Data Plane uses out-of-band metadata channels to enforce data scoping, action constraints, and tamper-proof auditing on autonomous AI agents.
citing papers explorer
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Measuring Real-World Prompt Injection Attacks in LLM-based Resume Screening
Roughly 1% of real resumes contain hidden prompt injections against LLM screeners, prevalence has risen over 1-2 years, and over 90% avoid explicit instructions.
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TRUSTDESC: Preventing Tool Poisoning in LLM Applications via Trusted Description Generation
TRUSTDESC prevents tool poisoning in LLM applications by automatically generating accurate tool descriptions from code via a three-stage pipeline of reachability analysis, description synthesis, and dynamic verification.
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"I Strongly Suspect This Website Is a Scam": Benchmarking PII Leakage and Detection without Defense in Autonomous Web Agents
New benchmark Scammer4U finds 54-93% critical PII leakage from frontier web agents on scam sites versus 0% on benign twins, plus a 30-point gap between verbalized suspicion and actual submission.
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ChainCaps: Composition-Safe Tool-Using Agents via Monotonic Capability Attenuation
ChainCaps prevents permission laundering in tool-using agents by enforcing monotonic capability attenuation through budget intersection, reducing attack success from 25-68% to 0-4.8% on 82 tasks while maintaining 96-100% benign performance.
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Aligning Provenance with Authorization: A Dual-Graph Defense for LLM Agents
AuthGraph aligns an execution provenance graph with a clean authorization graph to detect parameter-source deviations from user intent, reducing attack success rates to 1-2% on AgentDojo and AgentDyn while retaining most task utility.
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Behavioral Integrity Verification for AI Agent Skills
BIV audits AI agent skills at scale, finding 80% deviate from declared behavior on 49,943 skills and achieving 0.946 F1 for malicious skill detection.
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SkillScope: Toward Fine-Grained Least-Privilege Enforcement for Agent Skills
SkillScope detects over-privileged LLM agent skills with 94.53% F1 score via graph analysis and replay validation, finding 7,039 problematic skills in the wild and reducing violations by 88.56% while preserving task completion.
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From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills
SSL representation disentangles skill scheduling, structure, and logic using an LLM normalizer, improving skill discovery MRR@50 from 0.649 to 0.729 and risk assessment macro F1 from 0.409 to 0.509 over text baselines.
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Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility
Symbolic guardrails enforce 74% of specified safety policies in agent benchmarks and boost safety without hurting utility.
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PIArena: A Platform for Prompt Injection Evaluation
PIArena provides a unified evaluation platform for prompt injection attacks and defenses, featuring a new adaptive attack that reveals major weaknesses in existing protections.
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Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
A synthesis of 247 papers on LLM agent security identifies prompt injection and tool hijacking as dominant threats, notes weakly compositional defenses, and argues for trust boundaries and realistic evaluations.
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The Importance of Out-of-Band Metadata for Safe Autonomous Agents: The Redpanda Agentic Data Plane
The Redpanda Agentic Data Plane uses out-of-band metadata channels to enforce data scoping, action constraints, and tamper-proof auditing on autonomous AI agents.