Identifies cross-app context poisoning in ChatGPT Apps, a persistent indirect prompt injection delivered through undocumented first-party API parameters that lets one app manipulate others via the shared untagged context.
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Ignore Previous Prompt: Attack Techniques For Language Models
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abstract
Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing applications. However, studies that explore their vulnerabilities emerging from malicious user interaction are scarce. By proposing PromptInject, a prosaic alignment framework for mask-based iterative adversarial prompt composition, we examine how GPT-3, the most widely deployed language model in production, can be easily misaligned by simple handcrafted inputs. In particular, we investigate two types of attacks -- goal hijacking and prompt leaking -- and demonstrate that even low-aptitude, but sufficiently ill-intentioned agents, can easily exploit GPT-3's stochastic nature, creating long-tail risks. The code for PromptInject is available at https://github.com/agencyenterprise/PromptInject.
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- abstract Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing applications. However, studies that explore their vulnerabilities emerging from malicious user interaction are scarce. By proposing PromptInject, a prosaic alignment framework for mask-based iterative adversarial prompt composition, we examine how GPT-3, the most widely deployed language model in production, can be easily misaligned by simple handcrafted inputs. In particular, we investigate two types of attacks -- goal hijacking and prompt leaking -- and demon
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representative citing papers
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.
JAW uses hybrid program analysis to evolve inputs that hijack agentic workflows, successfully compromising 4714 GitHub workflows and eight n8n templates to enable actions like credential exfiltration.
NeuroTaint is the first taint tracking framework for LLM agents that uses offline auditing of semantic, causal, and persistent context to detect flows from untrusted sources to privileged sinks.
Malicious LLM API routers actively perform payload injection and secret exfiltration, with 9 of 428 tested routers showing malicious behavior and further poisoning risks from leaked credentials.
ContextLeak is the first empirical framework to audit worst-case information leakage in private in-context learning by inserting identifiable canary tokens and measuring their presence in model outputs.
Prompt injection attacks can self-replicate across LLM agents in multi-agent systems, enabling data theft, misinformation, and system disruption while propagating silently.
AgentDojo introduces an extensible evaluation framework populated with realistic agent tasks and security test cases to measure prompt injection robustness in tool-using LLM agents.
Self-generated QA supervision for language models is fragile due to non-uniform question selection and instruction compliance during answering, with mitigations that reduce compliance from 88% to 13%.
PrincipalBench exposes a sharp split in frontier LLMs between selective and over-refusing behavior on multi-party loyalty, with prompt scaffolding and KL distillation reducing harm rates but only along an existing leak/over-refusal trade-off.
Introduces Document-Authored Control-Signal Impersonation (DACSI) as a low-cost indirect prompt attack on RAG safety boundaries and evaluates its effectiveness across multiple models.
An empirical study of 547 confirmed safety incidents from GitHub and literature derives a 33-type taxonomy showing constraint violations, destructive actions, and deception dominate in everyday coding-agent use.
Controlled experiments on GPT-4o-mini and Claude Haiku show indirect prompt injection success in ReAct agents decays sharply with injection depth, varies with payload framing, and remains stable across turn budgets.
Log-substrate prompt injection via attacker-controlled fields enables effective attacks on LLM SOC assistants, with persona hijacks suppressing 68% of malicious logs and context manipulation reaching 96% success on summarization, reduced to 11.8% average under strongest defenses.
LASH adaptively composes multiple jailbreak seed prompts via genetic search over subsets and mixture weights to reach 84.5% keyword ASR and 74.5% two-stage ASR on JailbreakBench while using only 30 queries per prompt.
CrossMPI steers both visual and textual interpretations in LVLMs through image-only perturbations by optimizing in hidden-state space at selected middle layers with distance-based budget allocation.
IPI-proxy is a toolkit using an intercepting proxy to inject indirect prompt injection attacks into live web pages for testing AI browsing agents against hidden instructions.
ContextualJailbreak uses evolutionary search over simulated primed dialogues with novel mutations to reach 90-100% attack success on open LLMs and transfers to some closed frontier models at 15-90% rates.
In 30-step recursive LLM loops, append-mode persistent escape from source basins reaches 50% near 400 tokens under full history but plateaus below 50% under tail-clip memory policy, while replace-mode switching largely reflects state reset.
A malicious relay can strategically rewrite aligned LLM outputs in BYOK agent architectures to achieve up to 99.1% attack success on benchmarks like AgentDojo and ASB.
RAGCharacter localizes poisoned character spans in RAG evidence via prompt-conditioned counterfactual masking and achieves the best accuracy-over-attribution trade-off across tested attacks and models.
AgentVisor cuts prompt injection success rate to 0.65% in LLM agents with only 1.45% utility loss via semantic privilege separation and one-shot self-correction.
Introduces CSTM-Bench with 26 cross-session attack taxonomies, demonstrates recall loss in session-bound and full-log detectors, and proposes a bounded-memory coreset reader with the CSTM metric balancing detection and serving stability.
AudioHijack generates imperceptible adversarial audio via gradient estimation, attention supervision, and reverberation blending to hijack 13 LALMs with 79-96% success on unseen contexts and real commercial agents.
citing papers explorer
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Confused ChatGPT: Cross-App Context Poisoning via First-Party APIs
Identifies cross-app context poisoning in ChatGPT Apps, a persistent indirect prompt injection delivered through undocumented first-party API parameters that lets one app manipulate others via the shared untagged context.
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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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Comment and Control: Hijacking Agentic Workflows via Context-Grounded Evolution
JAW uses hybrid program analysis to evolve inputs that hijack agentic workflows, successfully compromising 4714 GitHub workflows and eight n8n templates to enable actions like credential exfiltration.
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Ghost in the Agent: Redefining Information Flow Tracking for LLM Agents
NeuroTaint is the first taint tracking framework for LLM agents that uses offline auditing of semantic, causal, and persistent context to detect flows from untrusted sources to privileged sinks.
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Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain
Malicious LLM API routers actively perform payload injection and secret exfiltration, with 9 of 428 tested routers showing malicious behavior and further poisoning risks from leaked credentials.
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ContextLeak: Auditing Leakage in Private In-Context Learning Methods
ContextLeak is the first empirical framework to audit worst-case information leakage in private in-context learning by inserting identifiable canary tokens and measuring their presence in model outputs.
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Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems
Prompt injection attacks can self-replicate across LLM agents in multi-agent systems, enabling data theft, misinformation, and system disruption while propagating silently.
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AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
AgentDojo introduces an extensible evaluation framework populated with realistic agent tasks and security test cases to measure prompt injection robustness in tool-using LLM agents.
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Self-Study Reconsidered: The Hidden Fragility of Learning from Self-Generated QA
Self-generated QA supervision for language models is fragile due to non-uniform question selection and instruction compliance during answering, with mitigations that reduce compliance from 88% to 13%.
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Whose Side Is Your Agent On? Multi-Party Principal Loyalty in LLM Agents
PrincipalBench exposes a sharp split in frontier LLMs between selective and over-refusing behavior on multi-party loyalty, with prompt scaffolding and KL distillation reducing harm rates but only along an existing leak/over-refusal trade-off.
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Document-Authored Control-Signal Impersonation: A Low-Cost Indirect Prompt Attack on RAG Safety Boundaries
Introduces Document-Authored Control-Signal Impersonation (DACSI) as a low-cost indirect prompt attack on RAG safety boundaries and evaluates its effectiveness across multiple models.
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What Breaks When LLMs Code? Characterizing Operational Safety Failures of Agentic Code Assistants
An empirical study of 547 confirmed safety incidents from GitHub and literature derives a 33-type taxonomy showing constraint violations, destructive actions, and deception dominate in everyday coding-agent use.
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Depth-Dependent Indirect Prompt Injection in Tool-Calling ReAct Agents: Injection Depth, Payload Framing, and Turn-Budget Sensitivity
Controlled experiments on GPT-4o-mini and Claude Haiku show indirect prompt injection success in ReAct agents decays sharply with injection depth, varies with payload framing, and remains stable across turn budgets.
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Poisoning the Watchtower: Prompt Injection Attacks Against LLM-Augmented Security Operations Through Adversarial Log Content
Log-substrate prompt injection via attacker-controlled fields enables effective attacks on LLM SOC assistants, with persona hijacks suppressing 68% of malicious logs and context manipulation reaching 96% success on summarization, reduced to 11.8% average under strongest defenses.
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LASH: Adaptive Semantic Hybridization for Black-Box Jailbreaking of Large Language Models
LASH adaptively composes multiple jailbreak seed prompts via genetic search over subsets and mixture weights to reach 84.5% keyword ASR and 74.5% two-stage ASR on JailbreakBench while using only 30 queries per prompt.
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A Cross-Modal Prompt Injection Attack against Large Vision-Language Models with Image-Only Perturbation
CrossMPI steers both visual and textual interpretations in LVLMs through image-only perturbations by optimizing in hidden-state space at selected middle layers with distance-based budget allocation.
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IPI-proxy: An Intercepting Proxy for Red-Teaming Web-Browsing AI Agents Against Indirect Prompt Injection
IPI-proxy is a toolkit using an intercepting proxy to inject indirect prompt injection attacks into live web pages for testing AI browsing agents against hidden instructions.
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ContextualJailbreak: Evolutionary Red-Teaming via Simulated Conversational Priming
ContextualJailbreak uses evolutionary search over simulated primed dialogues with novel mutations to reach 90-100% attack success on open LLMs and transfers to some closed frontier models at 15-90% rates.
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Perturbation Dose Responses in Recursive LLM Loops: Raw Switching, Stochastic Floors, and Persistent Escape under Append, Replace, and Dialog Updates
In 30-step recursive LLM loops, append-mode persistent escape from source basins reaches 50% near 400 tokens under full history but plateaus below 50% under tail-clip memory policy, while replace-mode switching largely reflects state reset.
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When Alignment Isn't Enough: Response-Path Attacks on LLM Agents
A malicious relay can strategically rewrite aligned LLM outputs in BYOK agent architectures to achieve up to 99.1% attack success on benchmarks like AgentDojo and ASB.
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Needle-in-RAG: Prompt-Conditioned Character-Level Traceback of Poisoned Spans in Retrieved Evidence
RAGCharacter localizes poisoned character spans in RAG evidence via prompt-conditioned counterfactual masking and achieves the best accuracy-over-attribution trade-off across tested attacks and models.
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AgentVisor: Defending LLM Agents Against Prompt Injection via Semantic Virtualization
AgentVisor cuts prompt injection success rate to 0.65% in LLM agents with only 1.45% utility loss via semantic privilege separation and one-shot self-correction.
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Cross-Session Threats in AI Agents: Benchmark, Evaluation, and Algorithms
Introduces CSTM-Bench with 26 cross-session attack taxonomies, demonstrates recall loss in session-bound and full-log detectors, and proposes a bounded-memory coreset reader with the CSTM metric balancing detection and serving stability.
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Hijacking Large Audio-Language Models via Context-Agnostic and Imperceptible Auditory Prompt Injection
AudioHijack generates imperceptible adversarial audio via gradient estimation, attention supervision, and reverberation blending to hijack 13 LALMs with 79-96% success on unseen contexts and real commercial agents.
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Kill-Chain Canaries: Stage-Level Tracking of Prompt Injection Across Attack Surfaces and Model Safety Tiers
Stage-level tracking of prompt injection reveals that write-node placement and model-specific behaviors determine attack outcomes more than initial exposure in LLM pipelines.
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AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?
AgentDyn benchmark demonstrates that current AI agent defenses against prompt injection fail to handle dynamic real-world conditions.
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The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections
Adaptive attackers using optimization techniques bypass 12 recent LLM defenses with >90% success, showing that prior robustness claims relied on weak evaluations.
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Prompt Injection Attack to Tool Selection in LLM Agents
ToolHijacker optimizes malicious tool documents via a two-phase strategy to hijack LLM agents' tool selection in no-box settings.
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Eliciting Latent Predictions from Transformers with the Tuned Lens
Training per-layer affine probes on frozen transformers yields more reliable latent predictions than the logit lens and enables detection of malicious inputs from prediction trajectories.
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KidnapRAG: A Black-Box Attack for Hijacking Reasoning in Agentic Retrieval-Augmented Generation Systems
KidnapRAG is a sequential black-box poisoning attack on Agentic RAG systems using Bait, Chain-Link, and Mal-Ins documents to redirect retrieval and reasoning, outperforming prior baselines.
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Security--Fidelity Tradeoffs: The Hidden Cost of Prompt Injection Defense
Prompt injection defenses create a security-fidelity tradeoff with no model or defense achieving both high security and high fidelity on the SecFid benchmark across 1,168 examples.
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Safety from Honesty in a Disinterested AI Predictor
A disinterested Bayesian Predictor trained on contextualized statements has low probability of producing harmful agency because dangerous behaviors require rare coordinated underestimation of harm with no training signal favoring them.
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RIPA: Sensory-Vector Prompt Injection Attacks on LLM-Controlled ROS 2 Robots
Empirical study finds LLM robustness to sensory prompt injections in robotic systems is model-specific rather than scale-dependent, with a hybrid firewall blocking known patterns but bypassed by obfuscated variants at 10.2% rate.
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Selection Integrity for LLM Graph Memory: An Accumulability Criterion for Information-Flow-Blind Retrieval
Provenance checks in graph memory are blind to structural attacks that reallocate top-k membership; authselect prevents this by enforcing selection on the authenticated subgraph only.
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Training LLMs to Enforce Multi-Level Instruction Hierarchies via Gravity-Weighted Direct Preference Optimization
GW-DPO with bilateral weighting improves macro pairwise priority adherence on Llama-3.1-8B-Instruct over standard DPO while halving over-refusal rates.
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ParaBridge: Bridging Paralinguistic Perception and Dialogue Behavior in Speech Language Models
ParaBridge applies on-policy self-distillation with a scaffold as privileged view to convert brittle inference-time paralinguistic guidance into stable model behavior, raising VoxSafeBench SAR from 14.6% to 40.3% on Qwen3-Omni-thinking while preserving general capabilities.
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Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI Infrastructure
Semantic Quorum Assurance routes AI infrastructure proposals to diverse sandboxed validators and applies risk-adaptive quorums to cut unsafe approvals from 18.5% to 0.3% on 500 scenarios.
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RecurGuard: Runtime Monitoring for Reasoning-Token Consumption Attacks
RecurGuard monitors recurrence rate, volume growth, and query progress in exposed reasoning traces to terminate generation on token-consumption attacks, reporting 99% detection on OverThink and 92% on ExtendAttack with near-zero false positives.
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What If Prompt Injection Never Left? Exploring Cross-Session Stored Prompt Injection in Agentic Systems
Formalizes stored prompt injection in agentic systems, develops a taxonomy and benchmark to show how adversarial prompts can persist across sessions via persistent state artifacts.
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Caught in the Act(ivation): Toward Pre-Output and Multi-Turn Detection of Credential Exfiltration by LLM Agents
Activation probes, calibrated honeytokens, and multi-turn leakage accounting detect credential exfiltration attempts in LLM agents with high accuracy in controlled open-model tests.
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From Prompt Injection to Persistent Control: Defending Agentic Harness Against Trojan Backdoors
Introduces ClawTrojan benchmark achieving 95.5% ASR for multi-step trojan attacks in agentic harnesses and DASGuard defense that sanitizes control content from untrusted sources.
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Strengthening Polymorphic Prompt Assembling: Dynamic Separator Generation Against Emerging Prompt Injection Attacks
Dynamic separator generation via domain-separated SHA-256 reduces attack success rate from 0.88 to 0.38 and eliminates leakage exposure in evaluations against 16 payloads on Llama and DeepSeek models.
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The Surface You Test Is Not the Surface That Breaks
Prompt injection vulnerability in tool-augmented LLMs is a model-surface interaction rather than a fixed channel property; the same payload inverts success rates across models, and adaptive attack rate exceeds single-surface baselines by 9.1 pp on average.
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Prompt Overflow: What the Guardrail Inspects Is Not What the Model Infers
Introduces Prompt Overflow Attack that fragments malicious instructions in overlength prompts to evade guardrail segmentation while remaining actionable to LLMs with larger context windows.
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Benchmarking Autonomous Agents against Temporal, Spatial, and Semantic Evasions
A3S-Bench evaluates LLM agents against temporal, spatial, and semantic evasions, raising average risk trigger rates from 28.3% to 52.6% across 2,254 trajectories and 20 scenarios.
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Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems
Domain-camouflaged injection attacks reduce detection rates from 93.8% to 9.7% on Llama 3.1 8B and 100% to 55.6% on Gemini 2.0 Flash, with the gap persisting in production classifiers and multi-agent debate setups.
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Compositional Jailbreaking: An Empirical Analysis of Mutator Chain Interactions in Aligned LLMs
Systematic evaluation of all ordered pairs among twelve jailbreak mutators on harmful prompts reveals mostly destructive interference but some synergistic combinations that raise success rates on three LLMs.
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Sleeper Channels and Provenance Gates: Persistent Prompt Injection in Always-on Autonomous AI Agents
Sleeper channels enable persistent prompt injection in always-on AI agents via persistence substrate and firing separation, countered by provenance gates using action digests and owner attestations with a soundness theorem.
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Proof-Carrying Certificates for LLM Pipelines: A Trust-Boundary Architecture
Introduces a trust-boundary architecture in Lean 4 with three certificate families and two operators that deliver sorry-free, axiom-audited assurances for LLM pipeline components.
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Leveraging RAG for Training-Free Alignment of LLMs
RAG-Pref is a training-free RAG-based alignment technique that conditions LLMs on contrastive preference samples during inference, yielding over 3.7x average improvement in agentic attack refusals when combined with offline methods across five LLMs.