{"id":"b0ad1853-c057-4099-a1d4-204bf84c4257","arxiv_id":"2505.06364","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"high","formal_verification":"none","parameter_count":5,"one_line_summary":"LATENT uses an LLM agent with detection feedback to insert stealthy analog Trojans into SPICE netlists, reporting narrower activation ranges and higher evasion against the SPICED detector.","lead":"LATENT is a new framework that uses a language-model agent to automatically insert malicious Trojan components into analog circuit designs, refining its attacks based on feedback from a detection tool. The authors report that the generated Trojans occupy a smaller input-voltage activation window than earlier analog Trojans, but the evaluation is tied to the same detector that guides the attack.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The stealth claim is a closed loop: the private, modified SPICED reimplementation is both the optimization reward and the evaluation oracle, so reported evasion may reflect reward overfitting rather than genuine stealth.","rationale":"The paper's strongest claim is conditional on SPICED being a valid proxy for real-world analog Trojan detection. The reader's weakest_assumption captures this. My independent read finds the same closed loop, with an additional aggravating detail: the authors modified the already-private detector's prompt (Section IV.A.2), so the attack is tailored not even to SPICED as published but to their own variant. I did not find a more fundamental flaw; the activation-range metric is under-specified but would matter even if the detector issue were fixed. Since the reader already recommended REJECT and my concern does not change that, I set verdict_should_be to UNCHANGED.","tokens_in":11774,"tokens_out":5246,"duration_ms":55745,"concrete_test":"Obtain the original SPICED implementation from the authors of [14] (or, failing that, a second independent analog-Trojan detector such as DAWN [13] running with default settings), and run the LATENT-generated netlists from Table I through it without any diagnosis feedback or prompt modifications. Compute Revade as in Section IV.C. If any of the 15 netlists has Revade substantially below 100% (e.g., below 80%), or if a non-trivial fraction of inserted components are flagged, then the reported stealth is an artifact of optimizing against the private modified oracle. As a control, also run the one-shot baseline netlists through the same independent detector to confirm the detector has real sensitivity.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—'stealthy, circuit-specific analog Trojans' with 15.74% average activation range and 11.3% performance degradation—requires that the generated Trojans genuinely evade analog Trojan detection. That condition is not met by the evidence. Section IV.C defines the evasion reward Revade as the fraction of agent-inserted components not in SPICED's suspect list, and Section V.C reports Revade as the headline stealth metric. SPICED is therefore simultaneously the objective the agent optimizes against and the yardstick used to measure success. Section IV.A.2 further states that SPICED's code is not public, that the authors re-implemented it, and that they modified its prompt to force consideration of resistor-based anomalies. The reported Revade=100% results are thus conditional on a private, author-modified oracle. An agent that has been given iterative diagnostic feedback from this exact detector can exploit its blind spots without demonstrating stealth against any independent detector or the original SPICED. This circularity alone invalidates the headline stealth claim; the activation-range metric is additionally under-specified (Section V.B), but the detector loop is the more fundamental problem.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces LATENT, an LLM-agent framework that inserts analog Trojans into SPICE netlists by iteratively selecting component types and node placements based on feedback from SPICED, an LLM-based analog Trojan detector. The framework uses HSPICE for simulation and a syntax checker, and it is evaluated on AMSNet circuits, a bandgap filter, and an LDO. The authors report an average Trojan-activation range of 15.74%, an average performance degradation of 11.3%, and high evasion rates (Revade) for LATENT compared with one-shot prompting and with prior A2 and DELTA Trojans.","tokens_in":11997,"tokens_out":4370,"duration_ms":43770,"significance":"If validated, LATENT would be a useful tool for generating diverse analog Trojan benchmarks and for stress-testing analog Trojan detection methods. The paper is among the first to apply agentic LLMs to analog Trojan insertion, and its attempt to include resistor-based Trojans, its feedback-driven insertion loop, and its comparison with prior Trojans are constructive contributions. However, the central stealth claim is undermined by the evaluation design: the detection oracle used to compute the evasion reward is the same private, author-modified SPICED reimplementation used to report evasion results. The paper also provides no code or data release, offers no variance analysis over LLM stochasticity, and leaves the activation-range metric under-specified. These issues prevent the reported results from supporting the paper's headline claims, although the underlying problem and framework idea are relevant to the hardware security community.","major_comments":[{"comment":"The stealth claim is supported only by Revade, which is computed from the same private, author-modified SPICED reimplementation that serves as the optimization reward. Section IV.A.2 explicitly states that the SPICED codebase is not public, that the authors re-implemented it, and that they modified its prompt to force consideration of resistor-based anomalies. Section IV.C defines Revade as the fraction of agent-inserted components not on SPICED's suspect list, and Table I reports these Revade values as the headline evasion metric. Because the agent iterates against this exact detector and then is evaluated by the same detector, the reported evasion results do not demonstrate stealth against the original SPICED or any independent detector; they show only that the agent learned to exploit the blind spots of the re-implemented detector. This circularity invalidates the central claim that LATENT generates stealthy Trojans.","section":"Section IV.A.2, IV.C, V.C"},{"comment":"The paper claims that LATENT generates 'functionally correct Trojan-inserted designs' and defines a stealthy analog Trojan as one that introduces negligible deviation in circuit performance in its dormant state, but no experiment verifies correct functionality or dormant-state performance. Table I and Table III report activation range, Revade, node impact, and area overhead, but no metric measures the output deviation of the Trojan-inserted netlist when the Trojan is not activated. Without such a check, a Trojan that breaks the circuit even when dormant—and is therefore not stealthy by the paper's own definition—would go undetected in the evaluation.","section":"Section I, II.A, V"},{"comment":"The activation-range metric is under-specified. The definition 'Trigger activation inputs / Total input space × 100%' does not state how the input space is discretized, what voltage sweep range and step size are used, or what output-deviation threshold defines an input as activating the Trojan. The headline average of 15.74% in Table III therefore cannot be reproduced or interpreted exactly as claimed, and comparisons of activation ranges across circuits and baselines are not well grounded.","section":"Section V.B"},{"comment":"All experimental results appear to come from a single run per circuit with GPT-4o-mini at temperature 0.3. The paper does not report variance across random seeds or repeated runs, so the stability of the agent's convergence (nit, LT, Revade) under LLM stochasticity is unknown. Since the feedback-driven agent is the core contribution, the absence of any stochasticity analysis weakens the empirical support for the framework's generality and reproducibility.","section":"Section V.A and Table I"}],"minor_comments":[{"comment":"The average LATENT Revade is reported as 94.4%, but several individual circuits (rows 5, 7, and 11) achieve Revade values of 66.6, 75, and 75, respectively; the paper should explain why these circuits fall short of the '100% evasion' criterion and how the average is consistent with the stealth contribution claim.","section":"Section V.C, Table I"},{"comment":"The discussion notes that only DC-activated Trojans are considered and that AC and small-signal analysis are deferred. This is a substantial scope limitation and should be stated in the abstract or introduction so that readers do not overgeneralize the stealth claims.","section":"Section V.F"},{"comment":"The formula for Revade appears inline without an equation number; numbering it would improve cross-referencing and precision in future discussions.","section":"Section IV.C"},{"comment":"In the introduction, 'a through evaluation' should be 'a thorough evaluation'.","section":"Section II.A"},{"comment":"The caption uses 'ReACT prompts,' while the body and Section III use 'ReAct'; the spelling should be made consistent.","section":"Figure 2 caption"},{"comment":"The 'Total time (s)' column does not equal the sum of the three preceding columns (e.g., 2.5 + 0.3 + 17.4 = 20.2, not 80.8); please clarify whether the total includes multiple iterations or additional setup overhead.","section":"Table II"}],"recommendation":"reject","confidential_remarks":"The paper addresses a timely and relevant problem, and the agentic framework concept has merit. However, the central stealth evaluation is circular because the same private, modified SPICED implementation is used as both the optimization reward and the evaluation oracle, and the paper provides no code or data to allow independent verification. If the authors could evaluate against the original SPICED or an independent detector, provide a precise definition of the activation-range metric, verify dormant-state functionality, and report variability across LLM runs, a substantially revised submission might be worth reconsidering."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Candid take: LATENT is a legitimately new application of LLM agents to analog Trojan insertion, and the framework itself is clearly described. The problem is that the paper's headline stealth result is circular. The authors re-implemented SPICED (which isn't public), modified its prompt to catch resistor anomalies, used that modified detector as the agent's reward signal, and then reported evasion against that same detector as their main result. That doesn't demonstrate stealth against real detectors; it demonstrates reward overfitting. This is the load-bearing issue, and it's not solved by the paper's acknowledgment of the reimplementation.\n\nThere's real value here. The thought-action-observation loop, the syntax checker, the use of HSPICE simulation, and the inclusion of resistor-based Trojans are sensible and go beyond prior static analog Trojans. The self-correction example is concrete evidence the agent is doing something nontrivial. The comparison with A2 and DELTA, though not perfectly apples-to-apples, is a reasonable attempt at positioning.\n\nThe other soft spots are secondary: the activation-range metric needs a precise definition of the input space discretization; there are no error bars over LLM stochasticity; and 'functionally correct' is asserted but never shown with dormant-state simulations. None of those would sink the paper on their own, but together with the circularity they mean the empirical claims are not currently supported.\n\nThe data in the tables are internally consistent and the citation practice is fair. The authors cite the prior analog Trojan and LLM-security literature appropriately, including their own SPICED papers (justified, given the direct reliance). I don't see invented entities or parameter-dredging red flags—the free parameters are disclosed.\n\nWho benefits: researchers in analog hardware security who want a starting point for adversarial evaluation of analog Trojan detectors. The paper deserves a serious referee, but the referee should insist on either code release, validation against an independent detector, or a significant downgrade of the claims. As it stands, I'd treat the 15.74% activation-range number as an artifact of the evaluation loop.\n\nRecommendation: send to peer review, with a clear expectation of major revision on the evaluation front.","headline":"Nice framework for LLM-driven analog Trojan insertion, but the headline stealth numbers rest on a closed loop: the authors' private, modified reimplementation of SPICED is both the optimization reward and the evaluation oracle.","tokens_in":12550,"tokens_out":3373,"would_cite":false,"duration_ms":31403,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"An LLM agent can automatically craft stealthy analog Trojans that hide across most input voltages and sharply degrade performance when triggered.","keywords":["analog Trojan","LLM agent","hardware security","SPICE netlist","Trojan detection evasion","feedback-driven attack","analog/mixed-signal IC","circuit-specific attack"],"falsifier":"Feed the LATENT-generated Trojan netlists to an independent analog-Trojan detector that was not part of the feedback loop and that has not been tuned by the authors; if any such detector consistently flags the inserted components, the claimed stealth is a property of the oracle, not the Trojans. A second check is to resimulate the dormant circuits with a voltage sweep finer than the one used in the paper and measure whether the average 15.74% activation range widens or the dormant-state output deviation exceeds the reported bounds.","tokens_in":11546,"feed_emoji":"🔌","tokens_out":6826,"duration_ms":60657,"temperature":0.7,"pith_summary":"LATENT claims that an LLM acting as an autonomous agent can insert analog Trojans into SPICE netlists that stay dormant across most of the input voltage range and only degrade circuit performance when triggered. The framework runs a thought-action-observation loop: the agent proposes a resistor, capacitor, or transistor modification, the modified netlist is simulated, and a detector's feedback tells the agent whether to keep or revise the choice. Across 15 analog benchmark circuits, the resulting Trojans activate over an average 15.74% of the input space and cause 11.3% average performance degradation, versus 34.3% and 43.2% activation ranges for two fixed-pattern baseline Trojans. If the result generalizes, analog Trojan insertion no longer needs manual circuit expertise, and detector evaluations must treat Trojans as adaptive adversaries rather than static templates.","feed_headline":"LLM agent crafts stealthy analog Trojans that evade detection","feed_subtitle":"Feedback from a detector keeps them dormant over most voltages, then performance drops 11.3% on trigger.","key_machinery":"The load-bearing mechanism is the feedback loop: the LLM agent's proposed modification is parsed by a syntax checker, simulated, then scored by the detector through $R_{\\text{evade}} = \\frac{|L_{\\text{agent}} \\cap L_{\\text{sus}}|}{|L_{\\text{agent}}|} \\times 100\\%$, the fraction of inserted Trojan lines that the detector flags. A score of 100% means the modification is accepted and the component type is retained; a lower score triggers a rethink using the detector's diagnosis report. The termination rules—three consecutive 100% scores or reaching $L_{\\max} = \\alpha N$ with $\\alpha = 0.6$—keep the search from over- or under-inserting. This closed loop, not the LLM's native code ability alone, is what converts one-shot generation into stealthy, circuit-tailored Trojan placement.","core_discovery":"The central claim is that analog Trojan insertion can be fully automated and made circuit-specific by coupling an LLM agent to a detection oracle. The agent selects components from resistors, capacitors, NMOS, and PMOS, inserts them at chosen nodes of the target netlist, simulates the modified design, and receives an evasion reward from a detector called SPICED. It keeps changes that score 100% for three consecutive iterations or until it reaches an upper bound on the number of inserted components. Across the evaluated circuits, the resulting Trojans average a 15.74% activation range and 11.3% output-voltage degradation, compared with activation ranges of 34.3% for the A2 baseline and 43.2% for DELTA, while cutting average area overhead by roughly two-thirds.","pith_inferences":["Because the agent optimizes against one detector, LATENT's stealth is probably detector-specific; swapping SPICED for a different analog-Trojan detector would produce different insertion patterns, so the framework is better read as a generator of detector-specific adversarial examples than as a proof of universal undetectability.","The restriction to DC-activated Trojans is an artifact of the detector's input format, not a limit of the method; extending the same feedback loop to AC/small-signal or temperature-based triggers, which the paper lists for future work, would likely widen the attack surface.","A testable corollary of the feedback design is that the one-shot baseline's high detection rate should be recoverable by removing the detector from the loop; if one-shot Trojans ever matched the feedback version's evasion, the loop itself, not component choice, would be the operative mechanism.","Read as a dual-use tool, LATENT could be repurposed to generate a diverse Trojan corpus for benchmarking detectors, which would directly address the paper's stated motivation that limited Trojan diversity blocks detection research."],"forward_implications":["Analog Trojan insertion becomes an automated, netlist-only attack: an adversary with access to a SPICE netlist and an LLM API can generate circuit-specific Trojans without deep analog design expertise.","Detection results obtained against static Trojan templates such as A2 and DELTA overstate real-world protection, because LATENT's feedback loop adapts to whatever blind spots the detector has.","The combination of a 15.74% average activation range and 11.3% performance degradation means a small number of inserted components can inflict serious harm while slipping past functional verification under typical voltages.","The upper-bound rule $L_{\\max} = \\alpha N$ lets an attacker scale Trojan count with circuit size, so the attack does not fade on larger designs; the LDO and bandgap-filter results show it also applies to larger netlists.","The reported runtimes of roughly one to two and a half minutes per circuit with a low-cost LLM make the framework practical for red-team evaluation at design time."],"supporting_citations":[{"why":"Supplies the LLM-based detector SPICED, re-implemented here, whose feedback drives the evasion loop and defines the stealth metric.","marker":"[14]"},{"why":"Provides the A2 analog Trojan baseline used for performance and activation-range comparison.","marker":"[6]"},{"why":"Provides the DELTA trigger-mechanism baseline used for comparison of activation range and area overhead.","marker":"[10]"},{"why":"Supplies the AMSNet open-source analog benchmark circuits that serve as target designs.","marker":"[40]"},{"why":"Defines the ReAct-style thought-action-observation structure on which the agentic workflow is built.","marker":"[38]"},{"why":"Demonstrates prior reinforcement-learning-driven Trojan generation in the digital domain, motivating the agent-driven analog extension.","marker":"[34]"},{"why":"Shows prior LLM-guided Trojan insertion in HDL, providing context for moving the same idea to analog netlists.","marker":"[27]"},{"why":"Supplies the LLM API pricing model used to estimate the framework's cost-effectiveness.","marker":"[41]"}],"fun_headline_variants":["LLM agent inserts stealthy analog Trojans, evades detection","AI crafts analog Trojans that stay dormant until trigger","LLM-generated analog Trojans shrink activation window to 15.7%","Feedback loop keeps LLM analog Trojans hidden from detectors","LLM automates stealthy analog Trojan design for chip attacks"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that SPICED, which the authors re-implemented and modified because the original code is not public, is a representative detector for real-world analog Trojans; LATENT optimizes against SPICED and then reports evasion from SPICED, so if that oracle is unrepresentative the stealth claim collapses.","fun_headline_variants_meta":{"raw":{"variants":["LLM agent inserts stealthy analog Trojans, evades detection","AI crafts analog Trojans that stay dormant until trigger","LLM-generated analog Trojans shrink activation window to 15.7%","Feedback loop keeps LLM analog Trojans hidden from detectors","LLM automates stealthy analog Trojan design for chip attacks"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000221,"raw_usage":{"total_tokens":1443,"prompt_tokens":929,"completion_tokens":514,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":545,"completion_tokens_details":{"reasoning_tokens":427}},"tokens_in":545,"tokens_out":514,"duration_ms":5190,"temperature":1.0,"reasoning_tokens":427,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T22:44:05.083317+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Feed the LATENT-generated Trojan netlists to an independent analog-Trojan detector that was not part of the feedback loop and that has not been tuned by the authors; if any such detector consistently flags the inserted components, the claimed stealth is a property of the oracle, not the Trojans. A second check is to resimulate the dormant circuits with a voltage sweep finer than the one used in the paper and measure whether the average 15.74% activation range widens or the dormant-state output deviation exceeds the reported bounds.","supporting_citations":[{"cited_title":"SPICED: Syntactical Bug and Trojan Pattern Identification in A/MS Circuits using LLM-Enhanced Detection,","cited_arxiv_id":null,"evidence_quote":"Supplies the LLM-based detector SPICED, re-implemented here, whose feedback drives the evasion loop and defines the stealth metric."},{"cited_title":"A2: Analog Malicious Hardware,","cited_arxiv_id":null,"evidence_quote":"Provides the A2 analog Trojan baseline used for performance and activation-range comparison."},{"cited_title":"DELTA: DEsigning a Stealthy Trigger Mechanism for Analog Hardware Trojans and Its Detection Analysis,","cited_arxiv_id":null,"evidence_quote":"Provides the DELTA trigger-mechanism baseline used for comparison of activation range and area overhead."},{"cited_title":"React: Synergizing Reasoning and Acting in Language Models,","cited_arxiv_id":null,"evidence_quote":"Defines the ReAct-style thought-action-observation structure on which the agentic workflow is built."},{"cited_title":"Harnessing the Power of General-Purpose LLMs in Hardware Trojan Design,","cited_arxiv_id":null,"evidence_quote":"Shows prior LLM-guided Trojan insertion in HDL, providing context for moving the same idea to analog netlists."},{"cited_title":"Pricing,","cited_arxiv_id":null,"evidence_quote":"Supplies the LLM API pricing model used to estimate the framework's cost-effectiveness."}],"review_version":1}