{"id":"7b7a18c1-f916-4cc7-a5f5-f7bd956c78af","arxiv_id":"2606.16821","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"SearchGEO evaluation of 13 LLM backends on 308 cases each shows attack success rates ranging from 0.0% (Claude-Sonnet-4.6) to 31.4% (Gemini-3-Flash), with vulnerability patterns and attack modes varying by model family.","lead":"The paper introduces SearchGEO, a controlled framework to test how LLM search agents can be tricked into endorsing attacker-manipulated web pages. Smart generalists should read it to understand a concrete safety risk in AI systems that synthesize web content into recommendations.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Realism of SearchGEO's five-mode taxonomy and manipulation pipeline as proxy for real-world attacker pages","rationale":"The reader’s weakest_assumption directly identifies the external-validity gap that determines whether the measured vulnerability patterns can be treated as evidence for the safety-evaluation recommendation. No internal inconsistency or statistical flaw is visible from the abstract; the load-bearing issue is whether the synthetic attacks generalize, which the proposed concrete_test would settle without requiring new model runs beyond the existing scaffold.","tokens_in":1716,"tokens_out":321,"duration_ms":27392,"concrete_test":"Extract the exact construction rules for each of the five attack modes from the methods section; apply those rules to a fresh sample of 50 real-world pages that an attacker could plausibly publish (e.g., low-authority blogs, forum posts); re-run the 13 backends and compare ASR to the original 308-case results. A >10-point absolute difference in any backend’s ASR falsifies the representativeness assumption.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The headline claim (varying ASR 0–31.4 % across backends, mode differences by family, scaffold effects) rests on the assumption that the controlled web-evidence manipulation pipeline and five attack modes generate endorsement-corruption instances that would arise from genuine attacker-published pages. The abstract presents these as a “controlled evaluation framework” but supplies no external validation that the synthetic manipulations match plausible attacker tactics, page structures, or retrieval contexts that an actual search agent would encounter on the open web.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces SearchGEO, a controlled evaluation framework combining a web-evidence manipulation pipeline and five-mode attack taxonomy to measure endorsement corruption in LLM-based search agents. It evaluates 13 LLM backends across 308 cases each, reporting overall attack success rates (ASR) ranging from 0.0% (Claude-Sonnet-4.6) to 31.4% (Gemini-3-Flash), with the strongest attack mode differing by model family, deployment scaffolds modulating ASR, and an auxiliary probe revealing splits (e.g., Claude over-rejecting vs. GPT over-trusting) when endorsement is framed as an install command. The work concludes that recommendation reliability under adversarial web content should be treated as a first-class safety evaluation dimension.","tokens_in":1828,"tokens_out":524,"duration_ms":49310,"significance":"If the results hold under representative attacks, the paper supplies valuable empirical data on model-specific vulnerabilities in agentic search systems, including scaffold effects and the auxiliary probe findings. The multi-backend, multi-case design and direct count-based metrics are strengths that could inform deployment and safety practices. The work is an empirical measurement study with no free parameters or circular definitions.","major_comments":[{"comment":"Abstract and SearchGEO framework description: The headline claims of varying ASR (0.0–31.4%) and model-family differences rest on the assumption that the five-mode taxonomy and manipulation pipeline produce realistic instances of endorsement corruption. The manuscript presents these as a 'controlled evaluation framework' but supplies no external validation, comparison to real attacker-published pages, or discussion of how the synthetic manipulations match plausible tactics, page structures, or retrieval contexts an actual search agent would encounter. This is load-bearing for interpreting the ASR numbers as measures of real-world vulnerability.","section":"Abstract and SearchGEO framework description"}],"minor_comments":[{"comment":"Evaluation section: The reported ASR values are presented as direct percentages without error bars, confidence intervals, or details on how the 308 cases were selected or whether exclusion rules were pre-registered, making it harder to assess the robustness of cross-backend comparisons.","section":"Evaluation section"},{"comment":"Overall manuscript: The auxiliary agent-skill probe is a useful addition, but its results would benefit from explicit comparison to the main five-mode results to clarify how the install-command framing relates to the primary taxonomy.","section":"Overall manuscript"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for highlighting the importance of validating the realism of the SearchGEO framework. The controlled synthetic design is central to our measurement approach, but we agree that additional discussion is warranted to help readers contextualize the ASR results. We address this point below and will revise the manuscript accordingly.","responses":[{"response":"We designed SearchGEO as a controlled evaluation to isolate the effects of specific attack modes on endorsement behavior while holding other variables fixed, which is necessary for reproducible, comparable measurements across 13 backends. The five-mode taxonomy draws from documented web manipulation techniques (e.g., SEO poisoning, fabricated authority signals, and content injection), and the pipeline generates instances that trigger the same retrieval and synthesis pathways used by real agents. We did not perform direct comparisons against live attacker pages, as constructing a representative, ethically sourced corpus of such pages at scale would introduce its own confounds and selection biases. We will revise the manuscript to (1) expand the framework description with explicit mappings from each attack mode to observed real-world tactics, (2) add a dedicated limitations subsection discussing ecological validity and the trade-off between control and realism, and (3) qualify the interpretation of ASR numbers as measures of vulnerability under the defined synthetic conditions rather than direct real-world prevalence estimates. These changes will make the load-bearing assumptions more transparent without altering the core empirical results.","revision_made":"yes","referee_comment":"The headline claims of varying ASR (0.0–31.4%) and model-family differences rest on the assumption that the five-mode taxonomy and manipulation pipeline produce realistic instances of endorsement corruption. The manuscript presents these as a 'controlled evaluation framework' but supplies no external validation, comparison to real attacker-published pages, or discussion of how the synthetic manipulations match plausible tactics, page structures, or retrieval contexts an actual search agent would encounter. This is load-bearing for interpreting the ASR numbers as measures of real-world vulnerability."}],"tokens_in":1399,"tokens_out":412,"duration_ms":29780,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main point is that this paper runs a controlled test on 13 LLM backends and reports attack success rates for getting agents to endorse manipulated web content, with Claude-Sonnet at 0% overall and Gemini-3-Flash at 31.4%, plus differences by attack mode and by deployment scaffold.\n\nWhat is new is the SearchGEO framework itself: a web-evidence manipulation pipeline, a five-mode attack taxonomy, output-level metrics, and the auxiliary install-command probe that splits otherwise strong models. The work is a straightforward count-based measurement study on 308 cases per backend with no equations or fitted parameters.\n\nIt does a clean job of showing that vulnerability is not uniform and that the same scaffold can help or hurt depending on the backend. The numbers give a concrete starting point for comparing models on this risk.\n\nThe soft spot is exactly the one in the stress-test note. The five modes and manipulation pipeline are presented as controlled, but the paper supplies no check that these instances match the page structures, retrieval contexts, or tactics an actual attacker would use on the open web. Without that grounding, the ASR figures are hard to read as deployment-relevant. The abstract does not describe any such validation step.\n\nThis is for groups working on agent safety evaluations. It deserves a serious referee because it brings measurable data to a practical concern, even if the attack design needs more justification before the numbers can be taken as strong evidence of real-world risk.","headline":"SearchGEO measures endorsement corruption in LLM search agents with per-backend ASR numbers from 0% to 31%, but the attack modes lack external validation against real web pages.","tokens_in":2326,"tokens_out":377,"would_cite":false,"duration_ms":37900,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"LLM search agents endorse attacker-manipulated web content at rates from zero to thirty-one percent depending on the backend.","keywords":["LLM search agents","endorsement vulnerability","web content manipulation","adversarial attacks","AI safety evaluation","attack success rate","model robustness"],"falsifier":"Direct measurement of whether live LLM search agents endorse content from actual attacker-published pages that use similar manipulation techniques in open-web searches.","tokens_in":2631,"feed_emoji":"","tokens_out":665,"duration_ms":44374,"temperature":0.7,"pith_summary":"The paper introduces SearchGEO, a framework that combines a web-evidence manipulation pipeline with a five-mode attack taxonomy to test whether LLM search agents turn manipulated pages into endorsed recommendations. It runs controlled evaluations on thirteen backends across 308 cases each and records attack success rates that range from zero percent on Claude-Sonnet-4.6 to 31.4 percent on Gemini-3-Flash. The strongest attack mode changes with model family, and the same deployment choices can raise or lower success rates differently for each backend. An auxiliary probe that turns endorsement into an install command further splits otherwise robust models into those that over-reject and those that over-trust. The authors therefore argue that recommendation reliability under adversarial web content should become a standard safety evaluation dimension.","feed_headline":"LLM search agents endorse manipulated web claims up to 31 percent","feed_subtitle":"Tests on 13 backends show model-specific vulnerabilities and that deployment choices change risk differently for each one.","key_machinery":"SearchGEO evaluation framework that combines a web-evidence manipulation pipeline, a five-mode attack taxonomy, and output-level metrics to quantify endorsement corruption in LLM search agents.","core_discovery":"Search agents built on different LLMs exhibit markedly different rates of endorsement corruption when presented with manipulated web evidence, with attack success rates ranging from 0.0 percent to 31.4 percent; the most effective attack mode depends on the model family, and the same deployment scaffold can increase or decrease vulnerability depending on the backend.","pith_inferences":["Safety benchmarks for search agents should routinely include tests against web manipulation for every backend.","The observed split in behavior suggests that differences in alignment or training affect how agents weigh manipulated evidence against user instructions.","Attackers could achieve higher success by selecting manipulation modes matched to the target model family.","Testing additional deployment scaffolds per backend could identify configurations that minimize vulnerability."],"forward_implications":["Attack success rates differ substantially across the thirteen evaluated LLM backends.","The most effective attack mode depends on the specific model family.","Deployment choices affect attack success rates differently for each backend.","An auxiliary skill probe reveals a split among robust models into over-rejectors and over-trusters.","Recommendation reliability under adversarial search content should be treated as a first-class safety dimension."],"fun_headline_variants":["LLM search agents show endorsement corruption from 0 to 31 percent","Backend determines LLM agent vulnerability to manipulated web pages","Attack success on search agents ranges 0 to 31 percent depending on model","Search agent reliability under attack varies widely by LLM backend"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The five-mode attack taxonomy and web-evidence manipulation pipeline create realistic instances of the endorsement corruption that real-world attackers would produce with published pages.","fun_headline_variants_meta":{"raw":{"variants":["LLM search agents show endorsement corruption from 0 to 31 percent","Backend determines LLM agent vulnerability to manipulated web pages","Attack success on search agents ranges 0 to 31 percent depending on model","Search agent reliability under attack varies widely by LLM backend"]},"model":"grok-4.3","cost_usd":0.004855,"raw_usage":{"total_tokens":2365,"prompt_tokens":631,"num_sources_used":0,"completion_tokens":69,"cost_in_usd_ticks":48549500,"prompt_tokens_details":{"text_tokens":631,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1665,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":631,"tokens_out":69,"duration_ms":22331,"temperature":1.0,"reasoning_tokens":1665,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-27T04:01:17.130843+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Direct measurement of whether live LLM search agents endorse content from actual attacker-published pages that use similar manipulation techniques in open-web searches.","supporting_citations":[],"review_version":1}