arXiv subjects

J Alex Corll

Publications and source records attributed to J Alex Corll.

3 recordsLinked to original sources

arXiv2026

Amplify, Don't Create: Temporal Accumulation for Slow-Burn Prompt Injection

Most prompt-injection detectors score a single event or message. Control-plane attacks against tool-using agents can instead distribute weak directives across a trajectory while keeping each event below threshold. We test whether a proxy-side temporal accumulator recovers this slow-burn signal by reducing frozen per-event scores to peak and CUSUM persistence statistics. To avoid circularity, grafts are generated against a held-out autoregressive cloaking target and then re-scored under a detector of record: a frozen char-ngram SVM plus an embedding-contrastive head. Only floor-met grafts bound to executed action edges and still sub-threshold under the detector of record enter the slow-burn endpoint. This is a boundary result, not a deployable detector. On concentrated attacks, trajectory-level accumulation beats the per-event foil under a clustered bootstrap (gap +0.092, 95% CI [+0.025, +0.155]), while persistence and peak are statistically tied. On git repo-exfil, density-four floor-met sub-threshold grafts add persistence mass that matched benign shams do not (persistence-delta AUC 0.708 over four attack survivors and six benign shams), while the matched peak-delta control does not separate attack from sham (AUC 0.417), localizing the effect to accumulated persistence rather than a single hot graft. The effect fails on broader clean-path actions (persistence-delta AUC 0.167), where the detector assigns attack and benign actions indistinguishable per-event scores, leaving no margin for CUSUM to bank. Independent powering is blocked by only three to four independent tasks. Temporal accumulation is therefore a narrow-band margin amplifier: it can bank elevated sub-threshold signal but cannot create margin where the per-event detector has none. As byproducts, we contribute a pseudo-replication warning and an independence-audit standard for agent-benchmark evaluation.

cs.CR↗

arXiv2026

The Mirror Design Pattern: Strict Data Geometry over Model Scale for Prompt Injection Detection

Prompt injection defenses are often framed as semantic understanding problems and delegated to increasingly large neural detectors. For the first screening layer, however, the requirements are different: the detector runs on every request and therefore must be fast, deterministic, non-promptable, and auditable. We introduce Mirror, a data-curation design pattern that organizes prompt injection corpora into matched positive and negative cells so that a classifier learns control-plane attack mechanics rather than incidental corpus shortcuts. Using 5,000 strictly curated open-source samples -- the largest corpus supportable under our public-data validity contract -- we define a 32-cell mirror topology, fill 31 of those cells with public data, train a sparse character n-gram linear SVM, compile its weights into a static Rust artifact, and obtain 95.97\% recall and 92.07\% F1 on a 524-case holdout at sub-millisecond latency with no external model runtime dependencies. On the same holdout, our next line of defense, a 22-million-parameter Prompt Guard~2 model reaches 44.35\% recall and 59.14\% F1 at 49\,ms median and 324\,ms p95 latency. Linear models still leave residual semantic ambiguities such as use-versus-mention for later pipeline layers, but within that scope our results show that for L1 prompt injection screening, strict data geometry can matter more than model scale.

cs.CR↗

arXiv2026

Peak + Accumulation: A Proxy-Level Scoring Formula for Multi-Turn LLM Attack Detection

Multi-turn prompt injection attacks distribute malicious intent across multiple conversation turns, exploiting the assumption that each turn is evaluated independently. While single-turn detection has been extensively studied, no published formula exists for aggregating per-turn pattern scores into a conversation-level risk score at the proxy layer -- without invoking an LLM. We identify a fundamental flaw in the intuitive weighted-average approach: it converges to the per-turn score regardless of turn count, meaning a 20-turn persistent attack scores identically to a single suspicious turn. Drawing on analogies from change-point detection (CUSUM), Bayesian belief updating, and security risk-based alerting, we propose peak + accumulation scoring -- a formula combining peak single-turn risk, persistence ratio, and category diversity. Evaluated on 10,654 multi-turn conversations -- 588 attacks sourced from WildJailbreak adversarial prompts and 10,066 benign conversations from WildChat -- the formula achieves 90.8% recall at 1.20% false positive rate with an F1 of 85.9%. A sensitivity analysis over the persistence parameter reveals a phase transition at rho ~ 0.4, where recall jumps 12 percentage points with negligible FPR increase. We release the scoring algorithm, pattern library, and evaluation harness as open source.

cs.CR↗