Prompt Injection

This is a defensive research paper, not an active exploit or newly disclosed vulnerability. It describes SkillGuard, a harness-level enforcement mechanism that restricts an LLM agent's future capabilities once untrusted data from a tool/skill enters its context, mitigating indirect prompt injection attacks. Because it presents a mitigation and is evaluated against known benchmark attacks (AgentDojo), it does not represent a new threat to flag as high risk; severity is low from a threat-intelligence perspective, though the underlying attack class it defends against remains relevant.

Updated Sep 1, 2026

This is a research paper describing SIR, a red-teaming methodology that automatically discovers and refines indirect prompt injection attacks against computer-use agents (CUAs) like Claude Opus and Gemini. It demonstrates that adaptive, self-improving attack composition significantly increases attack success rates over static hand-written injections, revealing that current CUA safety benchmarks likely underestimate real-world risk. No active exploit or in-the-wild campaign is described; this is disclosed academic security research intended to improve defenses.

Updated Sep 1, 2026

Researchers propose ECLIPSE, a framework that combines direct and indirect prompt injection to hijack long-horizon LLM agents (e.g., Codex, Claude Code) into executing multi-step malicious tool chains while evading detection. This is academic red-teaming research with a benchmark (LASE-Bench), not an observed in-the-wild exploit, but it demonstrates high attack success rates (up to 96.7%, 69.2% under defenses) against realistic multi-tool agent workflows.

Updated Sep 1, 2026

Eclipse Theia's AI Agent Mode file-editing tools fail to validate that model-supplied file paths stay within the workspace, allowing a malicious or manipulated model output to write or delete arbitrary files on the host. Since these tools execute without user confirmation, an attacker who can influence model output via indirect prompt injection can escalate to full code execution on the backend by overwriting files like shell startup scripts or SSH authorized_keys. This is a critical, high-impact vulnerability combining a classic path traversal flaw with the AI-specific attack surface of untrusted model-controlled tool arguments.

Updated Aug 31, 2026 · CVSS 8.8

A person reportedly embedded hidden AI instructions inside a legal filing, attempting to manipulate any AI system that later reads and processes the document into ruling or advising in their favor. This is a classic indirect prompt injection attack applied to a real-world professional document workflow rather than a novel technical exploit. Severity is moderate since the report describes a single documented incident without technical detail on payload sophistication or actual impact on a legal outcome.

Updated Aug 31, 2026

This is a defensive research paper, not an active exploit report. It describes 'Semantic Overlays,' a technique using trained adapters on a model's residual stream to help LLMs distinguish trusted instructions from untrusted content (e.g., tool output, user input), thereby mitigating prompt injection attacks. The underlying threat it addresses—LLMs losing track of span identity and being manipulated by text that mimics instructions—is a well-known, genuine issue, but the raw data itself presents a mitigation, not a novel attack.

Updated Aug 31, 2026

This is a defensive research paper, not an active exploit or newly disclosed vulnerability. It proposes ROPE, a system that tracks the provenance of tool call parameters to block indirect prompt injection (IPI) attacks where malicious instructions embedded in tool-read content hijack an agent's tool calls. The underlying threat class (IPI leading to unauthorized state-changing tool calls) is real and well-known, but this document itself describes a mitigation with strong empirical results, not a new attack.

Updated Aug 31, 2026

This is an academic research paper describing CAITLYN, a defensive middleware designed to detect and autonomously synthesize countermeasures against prompt injection attacks on LLM agents. It is not an active exploit or attack disclosure, but rather a proposed defense system; the paper also introduces a new benchmark ('Emerging') that reveals existing static defenses are vulnerable to novel injection techniques, which is a defensive-research finding rather than a new threat.

Updated Aug 31, 2026

This is an academic benchmark paper (not an active exploit) demonstrating that existing prompt injection defenses, which perform reasonably in short-context settings, largely fail when injected instructions are embedded in long documents like resumes, code, or emails. The authors show even simple heuristic injection attacks bypass state-of-the-art defenses at high success rates in realistic long-context LLM application scenarios. Severity is moderate because it is a research finding highlighting a systemic weakness rather than a disclosed exploit against a specific product or live system.

Updated Aug 31, 2026

This is an academic arXiv paper proposing a defensive framework to build and diagnose prompt injection classifiers, not an active exploit or vulnerability disclosure. It highlights that many confident classifier decisions can be flipped by removing a single token, revealing calibration weaknesses in existing detection models, which is useful research for improving defenses rather than a threat itself.

Updated Aug 28, 2026

This is a research paper (not an active exploit) demonstrating that tool-using LLM agents which reliably refuse overt prompt-injection exfiltration attempts can be reliably compromised (0% to 100% success) simply by reframing the same malicious instruction as an innocuous-looking integrity signature, config field, or trusted-looking hostname. The finding shows that current alignment-based and fine-tuned defenses (including a published defense, SecAlign) largely fail against reframed attacks, while destination allow-listing and planner/reader capability isolation reliably closed the gap.

Updated Aug 28, 2026

Agno versions up to 2.5.8 allow an unauthenticated attacker to achieve remote code execution by embedding malicious instructions in content the agent processes, such as a web page or document. Because LLM-generated arguments are passed unsanitized into exec(), runpy.run_path(), and subprocess.run(), the agent itself becomes the execution vector for attacker-controlled code. This is a critical, actively exploitable framework vulnerability with no authentication required.

Updated Aug 27, 2026

Researchers demonstrated that a simple malicious website summary request can hijack Claude Code Opus 5's 'Auto Mode' and achieve remote code execution with a 60-80% success rate. This directly contradicts Anthropic-commissioned third-party evaluations that reported a 0.00% prompt injection success rate, showing the safety classifier that replaced human approval prompts is far more bypassable in practice than claimed.

Updated Aug 27, 2026

This is a defensive research paper, not an active threat or exploit. It proposes 'Semantic Overlays,' a technique using learned adapters on a model's residual stream to help LLMs distinguish trusted instructions from untrusted content (e.g., tool outputs, user input), thereby mitigating prompt injection. The paper reports strong benchmark improvements against known prompt injection attack suites, and severity is low since it describes a mitigation, not a vulnerability or attack.

Updated Aug 26, 2026

This is a defensive research paper, not an active exploit: it identifies real trust-boundary gaps in the emerging W3C WebMCP proposal that let malicious web pages spoof tool ownership, manipulate tool lifecycles, and inject prompts via tool metadata/descriptions into browser-integrated LLM agents. The authors propose and empirically validate a dual-layer runtime (cryptographic tool provenance plus a quarantine/privileged agent split) that reduces these attack classes to near-zero success, while also disclosing a residual bypass via malicious tool names invoked before inspection.

Updated Aug 26, 2026

This is a defensive research paper, not an active exploit or vulnerability disclosure. It proposes Attnlocate, a runtime framework that analyzes LLM attention patterns to pinpoint which parts of external/tool-provided context are actually driving an agent's tool-calling decisions, helping detect indirect prompt injection and tool poisoning attacks. Since this describes a mitigation technique rather than a new threat, severity is low from a threat standpoint, though the underlying attack classes it addresses (indirect prompt injection, tool poisoning) remain real risks for agentic systems.

Updated Aug 26, 2026

This item is an academic research paper describing a new defensive fine-tuning technique (SecOPD) that reduces LLM susceptibility to adaptive prompt injection attacks, rather than describing a new vulnerability, active exploit, or attack technique. It is a mitigation contribution, not a threat disclosure, so it is classified as low severity from a threat-intelligence standpoint, though it is highly relevant context for understanding the ongoing prompt injection problem in agentic systems.

Updated Aug 25, 2026

This is an academic research paper describing a new defensive technique (AEGIS) against indirect prompt injection attacks in LLM-based agents, not a description of an active exploit or newly discovered vulnerability. It proposes using instruction-sensitive projectors and multi-layer consensus signals to detect malicious injected instructions while reducing over-refusal of legitimate inputs. The underlying threat class (indirect prompt injection) is well-known and already covered by existing threat intelligence; this submission itself introduces no new attack technique.

Updated Aug 25, 2026

This is a defensive research paper, not an active threat or exploit disclosure. It proposes TraceGrant, a governance framework that mitigates indirect prompt injection in networked LLM agents by binding tool execution to a pre-established 'Contract' derived from trusted user intent and verifying task completion against actual results. The underlying risk it addresses (indirect prompt injection redirecting tool calls) is well-known and already covered by existing threat models; no new vulnerability or attack technique is introduced here.

Updated Aug 24, 2026

This is an academic paper describing COPA, a defensive framework that uses continual preference optimization to improve LLM robustness against evolving prompt injection attacks over time. It is not an active exploit or vulnerability disclosure but a proposed mitigation technique, so no genuine new threat is introduced by this content.

Updated Aug 21, 2026