ENDOPROMPT is a white‑box technique that learns prefixes to degrade the utility of language model responses without producing harmful content. It uses clean victim continuations as pseudo‑references, performing local search and preference fitting on unlabeled instructions to generate utility‑degrading prefixes. In experiments on four instruction‑tuned models and seven benign benchmarks, ENDOPROMPT reduces utility by an average of 26.8 percentage points, with 27 of 28 benchmark cells showing negative change.
arXiv:2602. 05746v2 Announce Type: replace-cross Abstract: Prompt injection is a critical vulnerability in LLM agents, yet the strongest methods still rely on human red-teamers and hand-crafted prompts.
By Xin Chen, Jie Zhang, Florian Tram\`er
arXiv:2606. 15441v1 Announce Type: cross Abstract: Indirect prompt injection attacks hijack LLM-based agents by embedding malicious instructions in third-party data that the agent retrieves during task execution.
By Lipeng He, Yihan Wang, Jiawen Zhang, N. Asokan
The paper introduces a new benchmark for indirect prompt injection (IPI) that focuses on attacks which tamper with decisions already in progress rather than redirecting the task. In 90 validated cases across three tool‑use environments, attackers target up to six aspects of an agent’s decision‑making—objective, decision rules, available actions, expected consequences, observation reading, or state beliefs—while keeping the original task intact. The study finds that many defenses reduce objective‑targeting attacks but leave observation and state attacks largely effective, highlighting gaps in current mitigation strategies.
By Rongting Zhang, Martin Bertran, Sandeep Avula, Chia-Jung Lee, Vanessa Murdock
Guardrail models, which screen malicious prompts in LLM services, often use lightweight Transformers with short context windows and bucketed positional encodings. The study identifies a new failure mode called Overflip, where repeating a prompt causes the guardrail’s prediction to flip from malicious to benign as the sequence length increases. Experiments on nine popular guardrails show that 5 models exhibit MAL→BEN flips on 100 prompts, with flip rates ranging from 8% to 92% and first flips occurring between 2.6k and 9.4k tokens, highlighting a gradual attention dispersion distinct from traditional attention‑dilution attacks.
By Xu He, Chih-Hsuan Lin, Hung-Mao Chen, Junjie Xiong, Yan Zhai, Kun Sun
Large Language Models (LLMs) treat all context tokens uniformly, which creates prompt injection vulnerabilities and unreliable instruction execution. The paper introduces LLMON (LLM Object Notation), a post‑training framework that separates executable instructions from passive data and requires explicit identifier‑based execution. Using a 4.6 B‑token structured corpus and post‑training on Granite‑4.1 models, LLMON reduces indirect prompt‑injection success rates from 53–100 % to 0.02–5.6 % while maintaining general capabilities, and provides attention‑based interpretability metrics to diagnose failure modes.
By Basel Shbita, Michael Hind, Chad DeLuca, Nathan Fulton, Shubhi Asthana, Farhan Ahmed, Anisa Halimi, Yada Zhu, Mauro Martino, David Cox, Dan Gutfreund
arXiv:2610.06401v2 Announce Type: replace-cross
Abstract: Tool-using language-model agents are vulnerable to indirect prompt injection because they must act on untrusted external content. Existing tr...
By Mohamed Dhouib, Clement Elliker, Alexi Canesse, Ma\"el Jenny, Lucas-Andrei Thil, Mahammed El Sharkawy, Sonia Vanier, Elie Bursztein
arXiv:2609.36570v1 Announce Type: cross
Abstract: Indirect prompt injection makes an LLM agent treat untrusted retrieved text as instructions. We present CounterSteer, an inference-time defense that...
By Mark Russinovich
INFUSE is a new benchmark that tests indirect prompt injection (IPI) in large‑language‑model agents performing long‑horizon workflows. It contains 40 realistic tasks and 56 attack scenarios across five threat categories, with attacks injected through realistic interaction surfaces rather than just initial prompts. The study evaluated 19 LLMs, generating over 41,000 adversarial trajectories and found mean attack success rates of 19.6% (LLM evaluation) and 15.4% (heuristic verification), while prompt‑level hardening reduced success from 58.2% to 14.3%.
By Thuy Dung Nguyen, Jingyi Guo, Rana Seda Dubauskas, Sreepriya Damuluru, Taylor T. Johnson, Kevin Leach
arXiv:2606. 15057v1 Announce Type: cross Abstract: Indirect prompt injection (IPI) is a major security threat to LLM-powered agents.
By Xinhang Ma, Taoran Li, Chaowei Xiao, Zhiyuan Yu, Ning Zhang, Yevgeniy Vorobeychik
arXiv:2606. 27091v1 Announce Type: cross Abstract: LLMs fine-tuned for security classification are usually evaluated on held-out examples from the same distribution as their training data.
By Ryan Fetterman
Indirect prompt injection attacks hijack LLM-based agents by embedding malicious instructions in third-party data that the agent retrieves during task execution. Existing defenses report near-zero attack success rate on static benchmarks, yet recent adaptive evaluations show that these results collapse once the attacker is allowed to optimize against the deployed defense.